phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-12-17 17:12:43 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
llama-stack-mirror/docs/notebooks/Llama_Stack_Evals.ipynb
Xi Yan 73b6dd7af1 add link to colab
2024-12-12 17:54:41 -08:00

4813 lines
261 KiB
Text
Raw Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "hTIfyoGtjoWD"
},
"source": [
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing)\n",
"\n",
"# Llama Stack Evals\n",
"\n",
"This notebook will walk you through the main sets of APIs we offer with Llama Stack for supporting evaluations of your LLM applications with working examples to explore the possibilities that Llama Stack opens up for you.\n",
"\n",
"Read more about Llama Stack: https://llama-stack.readthedocs.io/en/latest/index.html\n",
"\n",
"Read more about the Llama Stack Evaluation flow: https://llama-stack.readthedocs.io/en/latest/cookbooks/evals.html\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bxs0FJ1ckGa6"
},
"source": [
"## 0. Bootstrapping Llama Stack Library\n",
"\n",
"##### 0.1. Prerequisite: Create TogetherAI account\n",
"\n",
"In order to run inference for the llama models, you will need to use an inference provider. Llama stack supports a number of inference [providers](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/inference).\n",
"\n",
"In this showcase, we will use [together.ai](https://www.together.ai/) as the inference provider. So, you would first get an API key from Together if you dont have one already.\n",
"You can also use Fireworks.ai or even Ollama if you would like to.\n",
"\n",
"\n",
"> **Note:** Set the API Key in the Secrets of this notebook as `TOGETHER_API_KEY`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "O9pGVlPIjpix",
"outputId": "e1fbe723-ae31-4630-eb80-4c4f6476d56f"
},
"outputs": [],
"source": [
"!pip install -U llama-stack"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "JQpLUSNjlGAM",
"outputId": "2f7fec97-5511-4cae-d51e-6d262fbca19c"
},
"outputs": [],
"source": [
"!llama stack build --template together --image-type venv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "KkT2qVeTlI-b",
"outputId": "9198fbfc-a126-4409-e2f5-5f5bf5cdf9a7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: `bwrap` is not available. Code interpreter tool will not work correctly.\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Using config <span style=\"color: #000080; text-decoration-color: #000080\">together</span>:\n",
"</pre>\n"
],
"text/plain": [
"Using config \u001b[34mtogether\u001b[0m:\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">apis:\n",
"- agents\n",
"- datasetio\n",
"- eval\n",
"- inference\n",
"- memory\n",
"- safety\n",
"- scoring\n",
"- telemetry\n",
"conda_env: together\n",
"datasets: <span style=\"font-weight: bold\">[]</span>\n",
"docker_image: null\n",
"eval_tasks: <span style=\"font-weight: bold\">[]</span>\n",
"image_name: together\n",
"memory_banks: <span style=\"font-weight: bold\">[]</span>\n",
"metadata_store:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/root/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">registry.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
"models:\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct\n",
" model_type: &amp;id001 !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-FP8\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
"providers:\n",
" agents:\n",
" - config:\n",
" persistence_store:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/root/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">agents_store.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" datasetio:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: huggingface\n",
" provider_type: remote::huggingface\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: localfs\n",
" provider_type: inline::localfs\n",
" eval:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" inference:\n",
" - config:\n",
" api_key: 4985b03e627419b2964d34b8519ac6c4319f094d1ffb4f45514b4eb87e5427a2\n",
" url: <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://api.together.xyz/v1</span>\n",
" provider_id: together\n",
" provider_type: remote::together\n",
" memory:\n",
" - config:\n",
" kvstore:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/root/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">faiss_store.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::fa</span>iss\n",
" safety:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: llama-guard\n",
" provider_type: inline::llama-guard\n",
" scoring:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: basic\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::ba</span>sic\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: llm-as-judge\n",
" provider_type: inline::llm-as-judge\n",
" - config:\n",
" openai_api_key: <span style=\"color: #008000; text-decoration-color: #008000\">''</span>\n",
" provider_id: braintrust\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::b</span>raintrust\n",
" telemetry:\n",
" - config:\n",
" service_name: llama-stack\n",
" sinks: sqlite\n",
" sqlite_db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/root/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">trace_store.db</span>\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
"scoring_fns: <span style=\"font-weight: bold\">[]</span>\n",
"shields:\n",
"- params: null\n",
" provider_id: null\n",
" provider_shield_id: null\n",
" shield_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
"version: <span style=\"color: #008000; text-decoration-color: #008000\">'2'</span>\n",
"\n",
"</pre>\n"
],
"text/plain": [
"apis:\n",
"- agents\n",
"- datasetio\n",
"- eval\n",
"- inference\n",
"- memory\n",
"- safety\n",
"- scoring\n",
"- telemetry\n",
"conda_env: together\n",
"datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"docker_image: null\n",
"eval_tasks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"image_name: together\n",
"memory_banks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"metadata_store:\n",
" db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
"models:\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct\n",
" model_type: &id001 !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-FP8\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision\n",
" model_type: *id001\n",
" provider_id: null\n",
" provider_model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
"providers:\n",
" agents:\n",
" - config:\n",
" persistence_store:\n",
" db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95magents_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" datasetio:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: huggingface\n",
" provider_type: remote::huggingface\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: localfs\n",
" provider_type: inline::localfs\n",
" eval:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" inference:\n",
" - config:\n",
" api_key: 4985b03e627419b2964d34b8519ac6c4319f094d1ffb4f45514b4eb87e5427a2\n",
" url: \u001b[4;94mhttps://api.together.xyz/v1\u001b[0m\n",
" provider_id: together\n",
" provider_type: remote::together\n",
" memory:\n",
" - config:\n",
" kvstore:\n",
" db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin\u001b[1;92me::fa\u001b[0miss\n",
" safety:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: llama-guard\n",
" provider_type: inline::llama-guard\n",
" scoring:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: basic\n",
" provider_type: inlin\u001b[1;92me::ba\u001b[0msic\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: llm-as-judge\n",
" provider_type: inline::llm-as-judge\n",
" - config:\n",
" openai_api_key: \u001b[32m''\u001b[0m\n",
" provider_id: braintrust\n",
" provider_type: inlin\u001b[1;92me::b\u001b[0mraintrust\n",
" telemetry:\n",
" - config:\n",
" service_name: llama-stack\n",
" sinks: sqlite\n",
" sqlite_db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mtrace_store.db\u001b[0m\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
"scoring_fns: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"shields:\n",
"- params: null\n",
" provider_id: null\n",
" provider_shield_id: null\n",
" shield_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
"version: \u001b[32m'2'\u001b[0m\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Model(identifier='meta-llama/Llama-3.1-405B-Instruct', metadata={}, provider_id='together', provider_resource_id='meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo', type='model', model_type='llm')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"from google.colab import userdata\n",
"\n",
"os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
"\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"client = LlamaStackAsLibraryClient(\"together\")\n",
"_ = client.initialize()\n",
"\n",
"# register 405B as LLM Judge model\n",
"client.models.register(\n",
" model_id=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
" provider_model_id=\"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\",\n",
" provider_id=\"together\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qwXHwHq4lS1s"
},
"source": [
"## 1. Open Benchmark Model Evaluation\n",
"\n",
"The first example walks you through how to evaluate a model candidate served by Llama Stack on open benchmarks. We will use the following benchmark:\n",
"\n",
"- [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models.\n",
"- [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dqXLFtcao1oI"
},
"source": [
"#### 1.1 Running MMMU\n",
"- We will use a pre-processed MMMU dataset from [llamastack/mmmu](https://huggingface.co/datasets/llamastack/mmmu). The preprocessing code is shown in in this [Github Gist](https://gist.github.com/yanxi0830/118e9c560227d27132a7fd10e2c92840). The dataset is obtained by transforming the original [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) dataset into correct format by `inference/chat-completion` API."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TC_IwIAQo4q-"
},
"outputs": [],
"source": [
"name = \"llamastack/mmmu\"\n",
"subset = \"Agriculture\"\n",
"split = \"dev\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 305,
"referenced_widgets": [
"feb82e061ee44283b4a46be858ef4cd7",
"78a2d2d4ee3f42f3be42ef4baa298561",
"ba5e6ca09f174ef3a348453cf5cfc24a",
"74b58e4647644c9daf9af488942fdaf4",
"d56e218958a041e286e80f24e400ab0b",
"cab80632b7564a9eb59583e09573c1ee",
"10c0d50d7c204de0b4c8e8f4d3ec0af5",
"626ef2f811ae4e119a0e85cebe92b91d",
"aef4172d916f40b0ab4ed09104e10f24",
"25529e7fd57049d2816d31f696eab1fd",
"093bdcb608cf4b4fa37b0032a3915187",
"c788d4e9e1e24dca9b6503689df9b631",
"d1587e2144bf46299c1bdec3ea96e4e7",
"500a072c09da41759cb2c942a16d8429",
"9785009392934e3bbb229e8781667cbc",
"84570fe2c2a54a068fb9b8cbc8b041a1",
"f9e579c58e3f4ae0bbb721dffa33bf0a",
"737116977f474ec0b68d88a40fd1086c",
"e6d6e516cd03452297d80c36376855dd",
"6ae0fadb3aeb4be18a9ab3279fb23145",
"fa4800a506ac480984d58933580df086",
"117468099dbc42fdaafc08207eaac7ab",
"44f585990aa244d8ba61f892dc1ccc1c",
"4fc59928a0544f95a4438b37d19ca437",
"fb644d47049f495397d0e60597c86ea3",
"78632694ff694442bc3fefc2cac2cbf5",
"083fd2549abd4b03bd41d8b92ec28f42",
"611d6472a58d419583acc416767a4c90",
"98c5ce434cff454eaaa3f0fd3498183a",
"3d0344a9cc744e369da1b6b7ea1b3be8",
"c452ccbf47a44073aee710175f707a7d",
"0218397c573e4b28bfb4ffa66464d50f",
"9b01bcd6e5174be2af19f457047017c8",
"4fed5720f30b4b3cbbc606a4f25e223b",
"6fa866b9971542739b0ed26d90ceac80",
"fe7553b513954cc68c427b5d9d260b33",
"4bc266d49a6741a88350e029d101425b",
"da57445f98e7427589962836c2b4287e",
"ad1fb86cc1f94fd9911eda03cf4a3783",
"fdefb51ad4c4418b98c5826126558011",
"179d41b80dc841e8a440482516b8bca5",
"22b1ecd2eff14770bcfb0c62d3d4213f",
"47f876cf41484d55b645e1e99337423a",
"340fbbb4982c460992c88885e79b47db",
"9659140487ca4d3ea799196d2c1ecf61",
"52150fd494d24eea89b5232077509355",
"04acde771d0a46699e1de07d9733d1a3",
"7b98103300814f3caea84266263b95a2",
"75f06408071c494f934bb909b84110d1",
"b09b2690894749339a9172e5ad0a9b75",
"cbed38801163438d891879b756f5baab",
"399a6417b23e4593bb244ec3abb6b46d",
"53a321f36b0d4e08a74a5bcfbd04434b",
"b8c0c8aaac0d4032bf5c673a43d084ab",
"d1f32499fa3f4795b92361637e23a9bb",
"c06f9a090fb54c74b947634bf6d11fa8",
"82991dcc80f14af9bd2e95f705980676",
"cd832e3842b945aabbb327856053f261",
"93ee645d54f34acdb0d15092d4a6f0d1",
"b77fe05bbcf84cdc8ef85b264ccd35f6",
"e17d286a965a49cfb8d5bf885865cb1e",
"ca015c1a0c1449e68edb282462435a3f",
"2932b06afde9468a976eb6bfb072b80e",
"d027c807ddc04f89bec41dc05fde7718",
"4ff3a6aaf706460bbba01b248b93000e",
"bfd75a39f0154c30adbaad1e2ca0f1e2",
"4f788a7920c346f3b42900825bd6711a",
"8e9358ec7d474808bb96c13e13489c67",
"f0dfeee2a8d64dedbc8ef55ad4e69932",
"9437b707bf1a4847a50aafeb4252dab5",
"f255707788704a76bd1651f26a22402d",
"3b70fa4e43ef4951862e119378c3c501",
"6c0a6a7fa8ca4e1c961a36305f0e7638",
"201bd914f9884e46b8e6df9d9900a6e8",
"f53b7ada01084e73bba6e14a95e2a534",
"d2029292327b488db02fd123ee2b75af",
"3e26bc24a3e44b4582f57913bdf98de4",
"9d2b6eabf7e14436b72bbf374b4a2a0a",
"b5d7cb5a6157449a850ef0e12e3d3eb7",
"c245d316bf9e44dabe5bfd1e47fc8d2e",
"963cf422ca894d82b0dd94c6165d41bf",
"78d0e2aa93674bbeb42bff87a23cce9b",
"12c6f1180eeb4e9eb9037ea5dd24ec8e",
"017a81d7160240a398947545963856f5",
"1cf8eeb8d81c4e8a8e95dd43296a78b9",
"5b0b5a3f79e94c51aae48fe0dd34ba0e",
"f5b34a743ce54fb591f25b04a2651d65",
"dec6399e2c5341aead66e1674d3e6c72",
"24e48376a72940679989a39a40bbe7f6",
"484df732051540859bc7ac9cecadc83c",
"4b33b1db50c34a2fa957d81a71a2a47f",
"e51d501e2f994baba40345ad632eabee",
"631a85e420b64e8cb6915af59c5ce08a",
"70af9cb2838c4a92bd67f8cb5c98d97f",
"158115266c284c4f8dbce3586151cbf1",
"ce5019b36cde44c58c5f596dbb59a2f8",
"b90d660ca8584ba1815a3c66b420c079",
"7c4d1de626784a59a7e0a33c24086186",
"21cf0e35ecd845a8b5e7c5ce241cf177"
]
},
"collapsed": true,
"id": "DJkmoG2kq1_P",
"outputId": "8493ee59-c6ff-4bb6-d787-f295944db1cf"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "feb82e061ee44283b4a46be858ef4cd7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"README.md: 0%| | 0.00/36.0k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c788d4e9e1e24dca9b6503689df9b631",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"dev-00000-of-00001.parquet: 0%| | 0.00/29.5M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "44f585990aa244d8ba61f892dc1ccc1c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"validation-00000-of-00001.parquet: 0%| | 0.00/165M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4fed5720f30b4b3cbbc606a4f25e223b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"test-00000-of-00003.parquet: 0%| | 0.00/461M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9659140487ca4d3ea799196d2c1ecf61",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"test-00001-of-00003.parquet: 0%| | 0.00/454M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c06f9a090fb54c74b947634bf6d11fa8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"test-00002-of-00003.parquet: 0%| | 0.00/471M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4f788a7920c346f3b42900825bd6711a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating dev split: 0%| | 0/5 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9d2b6eabf7e14436b72bbf374b4a2a0a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating validation split: 0%| | 0/30 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "24e48376a72940679989a39a40bbe7f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating test split: 0%| | 0/287 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import datasets\n",
"ds = datasets.load_dataset(path=name, name=subset, split=split)\n",
"ds = ds.select_columns([\"chat_completion_input\", \"input_query\", \"expected_answer\"])\n",
"eval_rows = ds.to_pandas().to_dict(orient=\"records\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sqBA5LbNq7Xm"
},
"source": [
"- **Run Evaluation on Model Candidate**\n",
" - Define a System Prompt\n",
" - Define an EvalCandidate\n",
" - Run evaluate on datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 441
},
"collapsed": true,
"id": "1r6qYTp9q5l7",
"outputId": "f1607a9b-c3a3-43cc-928f-0487d0438748"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [00:51<00:00, 10.28s/it]\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">EvaluateResponse</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">generations</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The Colorado potato beetle (Leptinotarsa decemlineata) is a significant pest of potatoes, causing damage to the leaves and stems of potato plants. The insect with black-colored antennae in the image is a Colorado potato beetle, which is known for its distinctive black and yellow stripes. On the other hand, the insect with tan-colored antennae is not a Colorado potato beetle and does not appear to be a pest of potatoes.\\n\\n*Answer*: B) The one with black coloured antennae'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'To determine the count of pathogens infecting this sunflower leaf, we need to analyze the image carefully. The image shows a sunflower leaf with several brown spots and patches on its surface. These brown spots and patches are indicative of fungal infections, which are common pathogens that affect sunflowers.\\n\\nUpon closer inspection, we can see that there are two distinct types of brown spots and patches on the leaf. One type is smaller and more circular in shape, while the other type is larger and more irregular in shape. This suggests that there may be two different pathogens infecting the leaf.\\n\\nHowever, without further information or testing, it is difficult to say for certain whether these two types of brown spots and patches are caused by different pathogens or if they are just different stages of the same infection. Therefore, based on the available information, the most likely answer is:\\n\\nAnswer: B) Two pathogens'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Based on the image, the most likely reason for the massive gum production on the trunks of these grapefruit trees in Cyprus is a fungal infection. The gummosis, or the production of gum, is a common symptom of fungal diseases in citrus trees, and it can be caused by various factors such as root damage, water stress, or nutrient deficiencies. However, in this case, the presence of the gum on the trunks of the trees suggests that the cause is more likely related to a fungal infection.\\n\\nAnswer: E) Fungal gummosis'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The correct answer is D) Most viruses have a specific relationship with their vectors.\\n\\nExplanation:\\n\\n* Laboratory work with micro manipulators can mimic the transmission of viruses, but this is not the primary method of virus transmission in nature.\\n* Not all plant-feeding insects can transmit viruses; only specific species that have evolved to transmit particular viruses are capable of doing so.\\n* Similarly, not all plant viruses can be transmitted by insects; some are transmitted through other means such as mechanical transmission or nematodes.\\n* The correct assertion is that most viruses have a specific relationship with their vectors, meaning that each virus is typically transmitted by a specific type of insect or vector.\\n\\nAnswer: D'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The petioles of this rhubarb are splitting, and we need to determine which of the listed issues would not be the cause. \\n\\nFirst, let's consider physiological problems (A). Rhubarb is a hardy plant, but it can still experience physiological issues due to factors like temperature fluctuations, water stress, or nutrient deficiencies. These issues could potentially cause the petioles to split.\\n\\nNext, let's look at phytoplasma infection (B). Phytoplasmas are bacteria-like organisms that can infect plants, causing a range of symptoms including yellowing or browning of leaves, stunted growth, and distorted or split petioles. So, phytoplasma infection could also be a possible cause.\\n\\nNow, let's consider animal damage (D). Animals like rabbits, deer, or rodents might feed on the rhubarb leaves, causing damage to the petioles and potentially leading to splitting.\\n\\nFinally, let's think about bacteria (E). Bacterial infections can cause a range of symptoms in plants, including soft rot, leaf spot, and petiole splitting. So, bacteria could also be a potential cause.\\n\\nBased on this analysis, it seems that all of the listed issues could potentially cause the petioles of this rhubarb to split. Therefore, the correct answer is:\\n\\nAnswer: C\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">scores</span>=<span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'basic::regex_parser_multiple_choice_answer'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'accuracy'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_correct'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_total'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.0</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>, <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>, <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>, <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span><span style=\"font-weight: bold\">}</span>, <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;35mEvaluateResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mgenerations\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The Colorado potato beetle \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLeptinotarsa decemlineata\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is a significant pest of potatoes, causing damage to the leaves and stems of potato plants. The insect with black-colored antennae in the image is a Colorado potato beetle, which is known for its distinctive black and yellow stripes. On the other hand, the insect with tan-colored antennae is not a Colorado potato beetle and does not appear to be a pest of potatoes.\\n\\n*Answer*: B\u001b[0m\u001b[32m)\u001b[0m\u001b[32m The one with black coloured antennae'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'To determine the count of pathogens infecting this sunflower leaf, we need to analyze the image carefully. The image shows a sunflower leaf with several brown spots and patches on its surface. These brown spots and patches are indicative of fungal infections, which are common pathogens that affect sunflowers.\\n\\nUpon closer inspection, we can see that there are two distinct types of brown spots and patches on the leaf. One type is smaller and more circular in shape, while the other type is larger and more irregular in shape. This suggests that there may be two different pathogens infecting the leaf.\\n\\nHowever, without further information or testing, it is difficult to say for certain whether these two types of brown spots and patches are caused by different pathogens or if they are just different stages of the same infection. Therefore, based on the available information, the most likely answer is:\\n\\nAnswer: B\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Two pathogens'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Based on the image, the most likely reason for the massive gum production on the trunks of these grapefruit trees in Cyprus is a fungal infection. The gummosis, or the production of gum, is a common symptom of fungal diseases in citrus trees, and it can be caused by various factors such as root damage, water stress, or nutrient deficiencies. However, in this case, the presence of the gum on the trunks of the trees suggests that the cause is more likely related to a fungal infection.\\n\\nAnswer: E\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Fungal gummosis'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The correct answer is D\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Most viruses have a specific relationship with their vectors.\\n\\nExplanation:\\n\\n* Laboratory work with micro manipulators can mimic the transmission of viruses, but this is not the primary method of virus transmission in nature.\\n* Not all plant-feeding insects can transmit viruses; only specific species that have evolved to transmit particular viruses are capable of doing so.\\n* Similarly, not all plant viruses can be transmitted by insects; some are transmitted through other means such as mechanical transmission or nematodes.\\n* The correct assertion is that most viruses have a specific relationship with their vectors, meaning that each virus is typically transmitted by a specific type of insect or vector.\\n\\nAnswer: D'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The petioles of this rhubarb are splitting, and we need to determine which of the listed issues would not be the cause. \\n\\nFirst, let's consider physiological problems \u001b[0m\u001b[32m(\u001b[0m\u001b[32mA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. Rhubarb is a hardy plant, but it can still experience physiological issues due to factors like temperature fluctuations, water stress, or nutrient deficiencies. These issues could potentially cause the petioles to split.\\n\\nNext, let's look at phytoplasma infection \u001b[0m\u001b[32m(\u001b[0m\u001b[32mB\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. Phytoplasmas are bacteria-like organisms that can infect plants, causing a range of symptoms including yellowing or browning of leaves, stunted growth, and distorted or split petioles. So, phytoplasma infection could also be a possible cause.\\n\\nNow, let's consider animal damage \u001b[0m\u001b[32m(\u001b[0m\u001b[32mD\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. Animals like rabbits, deer, or rodents might feed on the rhubarb leaves, causing damage to the petioles and potentially leading to splitting.\\n\\nFinally, let's think about bacteria \u001b[0m\u001b[32m(\u001b[0m\u001b[32mE\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. Bacterial infections can cause a range of symptoms in plants, including soft rot, leaf spot, and petiole splitting. So, bacteria could also be a potential cause.\\n\\nBased on this analysis, it seems that all of the listed issues could potentially cause the petioles of this rhubarb to split. Therefore, the correct answer is:\\n\\nAnswer: C\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mscores\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'basic::regex_parser_multiple_choice_answer'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m0.2\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m5.0\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tqdm import tqdm\n",
"from rich.pretty import pprint\n",
"\n",
"SYSTEM_PROMPT_TEMPLATE = \"\"\"\n",
"You are an expert in {subject} whose job is to answer questions from the user using images.\n",
"\n",
"First, reason about the correct answer.\n",
"\n",
"Then write the answer in the following format where X is exactly one of A,B,C,D:\n",
"\n",
"Answer: X\n",
"\n",
"Make sure X is one of A,B,C,D.\n",
"\n",
"If you are uncertain of the correct answer, guess the most likely one.\n",
"\"\"\"\n",
"\n",
"system_message = {\n",
" \"role\": \"system\",\n",
" \"content\": SYSTEM_PROMPT_TEMPLATE.format(subject=subset),\n",
"}\n",
"\n",
"client.eval_tasks.register(\n",
" eval_task_id=\"meta-reference::mmmu\",\n",
" dataset_id=f\"mmmu-{subset}-{split}\",\n",
" scoring_functions=[\"basic::regex_parser_multiple_choice_answer\"]\n",
")\n",
"\n",
"response = client.eval.evaluate_rows(\n",
" task_id=\"meta-reference::mmmu\",\n",
" input_rows=eval_rows,\n",
" scoring_functions=[\"basic::regex_parser_multiple_choice_answer\"],\n",
" task_config={\n",
" \"type\": \"benchmark\",\n",
" \"eval_candidate\": {\n",
" \"type\": \"model\",\n",
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0.0,\n",
" \"max_tokens\": 4096,\n",
" \"top_p\": 0.9,\n",
" \"repeat_penalty\": 1.0,\n",
" },\n",
" \"system_message\": system_message\n",
" }\n",
" }\n",
")\n",
"pprint(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vYlb9wKzwg-s"
},
"source": [
"#### 1.2. Running SimpleQA\n",
"- We will use a pre-processed SimpleQA dataset from [llamastack/evals](https://huggingface.co/datasets/llamastack/evals/viewer/evals__simpleqa) which is obtained by transforming the input query into correct format accepted by `inference/chat-completion` API.\n",
"- Since we will be using this same dataset in our next example for Agentic evaluation, we will register it using the `/datasets` API, and interact with it through `/datasetio` API."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HXmZf3Ymw-aX"
},
"outputs": [],
"source": [
"simpleqa_dataset_id = \"huggingface::simpleqa\"\n",
"\n",
"_ = client.datasets.register(\n",
" dataset_id=simpleqa_dataset_id,\n",
" provider_id=\"huggingface\",\n",
" url={\"uri\": \"https://huggingface.co/datasets/llamastack/evals\"},\n",
" metadata={\n",
" \"path\": \"llamastack/evals\",\n",
" \"name\": \"evals__simpleqa\",\n",
" \"split\": \"train\",\n",
" },\n",
" dataset_schema={\n",
" \"input_query\": {\"type\": \"string\"},\n",
" \"expected_answer\": {\"type\": \"string\"},\n",
" \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gc8azb4Rxr5J"
},
"outputs": [],
"source": [
"eval_rows = client.datasetio.get_rows_paginated(\n",
" dataset_id=simpleqa_dataset_id,\n",
" rows_in_page=5,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 506
},
"id": "zSYAUnBUyRaG",
"outputId": "038cf42f-4e3c-4053-b3c4-cf16547483dd"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [00:48<00:00, 9.68s/it]\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">EvaluateResponse</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">generations</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The recipient of the IEEE Frank Rosenblatt Award in 2010 was Vladimir Vapnik'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"I am unable to verify who was awarded the Oceanography Society's Jerlov Award in 2018.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Radcliffe College was a women's liberal arts college, but it has since been integrated into Harvard University.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The Leipzig 1877 tournament was organized in the honor of 50th anniversary of the first chess club in Germany (the Leipzig Chess Club's) founding and of the 50th anniversary of Paul Morphy's birth\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Karl Küchler's 1908 guidebook states that Empress Elizabeth of Austria's favorite sculpture, which was made for her villa Achilleion at Corfu, depicted 'Dying Achilles'.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">scores</span>=<span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'llm-as-judge::405b-simpleqa'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"font-weight: bold\">]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;35mEvaluateResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mgenerations\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The recipient of the IEEE Frank Rosenblatt Award in 2010 was Vladimir Vapnik'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"I am unable to verify who was awarded the Oceanography Society's Jerlov Award in 2018.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"Radcliffe College was a women's liberal arts college, but it has since been integrated into Harvard University.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The Leipzig 1877 tournament was organized in the honor of 50th anniversary of the first chess club in Germany \u001b[0m\u001b[32m(\u001b[0m\u001b[32mthe Leipzig Chess Club's\u001b[0m\u001b[32m)\u001b[0m\u001b[32m founding and of the 50th anniversary of Paul Morphy's birth\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"Karl Küchler's 1908 guidebook states that Empress Elizabeth of Austria's favorite sculpture, which was made for her villa Achilleion at Corfu, depicted 'Dying Achilles'.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mscores\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'llm-as-judge::405b-simpleqa'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'B'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'A'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'A'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'B'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'B'\u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"client.eval_tasks.register(\n",
" eval_task_id=\"meta-reference::simpleqa\",\n",
" dataset_id=simpleqa_dataset_id,\n",
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"]\n",
")\n",
"\n",
"response = client.eval.evaluate_rows(\n",
" task_id=\"meta-reference::simpleqa\",\n",
" input_rows=eval_rows.rows,\n",
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"],\n",
" task_config={\n",
" \"type\": \"benchmark\",\n",
" \"eval_candidate\": {\n",
" \"type\": \"model\",\n",
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0.0,\n",
" \"max_tokens\": 4096,\n",
" \"top_p\": 0.9,\n",
" \"repeat_penalty\": 1.0,\n",
" },\n",
" }\n",
" }\n",
")\n",
"pprint(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eyziqe_Em6d6"
},
"source": [
"## 2. Agentic Evaluation\n",
"\n",
"- In this example, we will demonstrate how to evaluate a agent candidate served by Llama Stack via `/agent` API.\n",
"\n",
"- We will continue to use the SimpleQA dataset we used in previous example.\n",
"\n",
"- Instead of running evaluation on model, we will run the evaluation on a Search Agent with access to search tool. We will define our agent evaluation candidate through `AgentConfig`.\n",
"\n",
"> You will need to set the `TAVILY_SEARCH_API_KEY` in Secrets of this notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 538
},
"id": "mxLCsP4MvFqP",
"outputId": "8be2a32f-2a47-4443-8992-0000c23ca678"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"5it [00:26, 5.29s/it]\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">EvaluateResponse</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">generations</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"I'm sorry but I cannot find the recipient of the IEEE Frank Rosenblatt Award in 2010.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"I'm not sure who was awarded the Oceanography Society's Jerlov Award in 2018. Let me search for the information.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The women's liberal arts college in Cambridge, Massachusetts is called Radcliffe College. However, in 1999, it merged with Harvard University and is now known as the Radcliffe Institute for Advanced Study at Harvard University.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The 1877 Leipzig tournament was organized in honor of Anderssen, a German chess master.'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Empress Elizabeth of Austria's favorite sculpture, made for her villa Achilleion at Corfu, depicted Achilles.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">scores</span>=<span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'llm-as-judge::405b-simpleqa'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C.'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"font-weight: bold\">]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;35mEvaluateResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mgenerations\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"I'm sorry but I cannot find the recipient of the IEEE Frank Rosenblatt Award in 2010.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"I'm not sure who was awarded the Oceanography Society's Jerlov Award in 2018. Let me search for the information.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The women's liberal arts college in Cambridge, Massachusetts is called Radcliffe College. However, in 1999, it merged with Harvard University and is now known as the Radcliffe Institute for Advanced Study at Harvard University.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The 1877 Leipzig tournament was organized in honor of Anderssen, a German chess master.'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"Empress Elizabeth of Austria's favorite sculpture, made for her villa Achilleion at Corfu, depicted Achilles.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mscores\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'llm-as-judge::405b-simpleqa'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C.'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'A'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'A'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'A'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'A'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'B'\u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"agent_config = {\n",
" \"model\": \"meta-llama/Llama-3.1-405B-Instruct\",\n",
" \"instructions\": \"You are a helpful assistant\",\n",
" \"sampling_params\": {\n",
" \"strategy\": \"greedy\",\n",
" \"temperature\": 0.0,\n",
" \"top_p\": 0.95,\n",
" },\n",
" \"tools\": [\n",
" {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
" }\n",
" ],\n",
" \"tool_choice\": \"auto\",\n",
" \"tool_prompt_format\": \"json\",\n",
" \"input_shields\": [],\n",
" \"output_shields\": [],\n",
" \"enable_session_persistence\": False\n",
"}\n",
"\n",
"response = client.eval.evaluate_rows(\n",
" task_id=\"meta-reference::simpleqa\",\n",
" input_rows=eval_rows.rows,\n",
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"],\n",
" task_config={\n",
" \"type\": \"benchmark\",\n",
" \"eval_candidate\": {\n",
" \"type\": \"agent\",\n",
" \"config\": agent_config,\n",
" }\n",
" }\n",
")\n",
"pprint(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1yxrvqi8lhsM"
},
"source": [
"## 3. Agentic Application Dataset Scoring\n",
"\n",
"Llama Stack offers a library of scoring functions and the `/scoring` API, allowing you to run evaluations on your pre-annotated AI application datasets.\n",
"\n",
"In this example, we will work with an example RAG dataset and use LLM-As-Judge with custom judge prompt for scoring. Please checkout our [Llama Stack Playground](https://llama-stack.readthedocs.io/en/latest/playground/index.html) for an interactive interface to upload datasets and run scorings."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 293
},
"id": "pCkgSOVklfqo",
"outputId": "451b6fa1-6726-4244-9ad4-937b6b453a43"
},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringScoreResponse</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">results</span>=<span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'llm-as-judge::base'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE contains a detailed list of topics related to LoRA, while the EXPECTED_RESPONSE is a more general response, mentioning only \"LoRA\". This indicates that the GENERATED_RESPONSE provides more information than the EXPECTED_RESPONSE, but does not contradict it.'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"font-weight: bold\">]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'basic::subset_of'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'accuracy'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_correct'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_total'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span><span style=\"font-weight: bold\">}]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;35mScoringScoreResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mresults\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'llm-as-judge::base'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE contains a detailed list of topics related to LoRA, while the EXPECTED_RESPONSE is a more general response, mentioning only \"LoRA\". This indicates that the GENERATED_RESPONSE provides more information than the EXPECTED_RESPONSE, but does not contradict it.'\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'basic::subset_of'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import rich\n",
"from rich.pretty import pprint\n",
"\n",
"judge_model_id = \"meta-llama/Llama-3.1-405B-Instruct-FP8\"\n",
"\n",
"JUDGE_PROMPT = \"\"\"\n",
"Given a QUESTION and GENERATED_RESPONSE and EXPECTED_RESPONSE.\n",
"\n",
"Compare the factual content of the GENERATED_RESPONSE with the EXPECTED_RESPONSE. Ignore any differences in style, grammar, or punctuation.\n",
" The GENERATED_RESPONSE may either be a subset or superset of the EXPECTED_RESPONSE, or it may conflict with it. Determine which case applies. Answer the question by selecting one of the following options:\n",
" (A) The GENERATED_RESPONSE is a subset of the EXPECTED_RESPONSE and is fully consistent with it.\n",
" (B) The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it.\n",
" (C) The GENERATED_RESPONSE contains all the same details as the EXPECTED_RESPONSE.\n",
" (D) There is a disagreement between the GENERATED_RESPONSE and the EXPECTED_RESPONSE.\n",
" (E) The answers differ, but these differences don't matter from the perspective of factuality.\n",
"\n",
"Give your answer in the format \"Answer: One of ABCDE, Explanation: \".\n",
"\n",
"Your actual task:\n",
"\n",
"QUESTION: {input_query}\n",
"GENERATED_RESPONSE: {generated_answer}\n",
"EXPECTED_RESPONSE: {expected_answer}\n",
"\"\"\"\n",
"\n",
"input_query = \"What are the top 5 topics that were explained? Only list succinct bullet points.\"\n",
"generated_answer = \"\"\"\n",
"Here are the top 5 topics that were explained in the documentation for Torchtune:\n",
"\n",
"* What is LoRA and how does it work?\n",
"* Fine-tuning with LoRA: memory savings and parameter-efficient finetuning\n",
"* Running a LoRA finetune with Torchtune: overview and recipe\n",
"* Experimenting with different LoRA configurations: rank, alpha, and attention modules\n",
"* LoRA finetuning\n",
"\"\"\"\n",
"expected_answer = \"\"\"LoRA\"\"\"\n",
"\n",
"dataset_rows = [\n",
" {\n",
" \"input_query\": input_query,\n",
" \"generated_answer\": generated_answer,\n",
" \"expected_answer\": expected_answer,\n",
" },\n",
"]\n",
"\n",
"scoring_params = {\n",
" \"llm-as-judge::base\": {\n",
" \"judge_model\": judge_model_id,\n",
" \"prompt_template\": JUDGE_PROMPT,\n",
" \"type\": \"llm_as_judge\",\n",
" \"judge_score_regexes\": [\"Answer: (A|B|C|D|E)\"],\n",
" },\n",
" \"basic::subset_of\": None,\n",
"}\n",
"\n",
"response = client.scoring.score(input_rows=dataset_rows, scoring_functions=scoring_params)\n",
"pprint(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jLQ7UzM8nTG3"
},
"source": [
"## 4. Online Evaluation Dataset Collection Using Telemetry\n",
"\n",
"- Llama Stack offers built-in telemetry to collect traces and data about your agentic application.\n",
"- In this example, we will show how to build an Agent with Llama Stack, and query the agent's traces into an online dataset that can be used for evaluation. \n",
"- Please see our [Llama Stack Showcase](https://colab.research.google.com/drive/1F2ksmkoGQPa4pzRjMOE6BXWeOxWFIW6n) notebook for more examples on building agents."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_JueJAKyJR5m"
},
"source": [
"##### 🚧 Patches 🚧\n",
"- The following cells are temporary patches to get `telemetry` working."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "klPkK1t7CzIY",
"outputId": "ab0c1490-7fa6-446c-8e35-7b42f57e8a04"
},
"outputs": [],
"source": [
"# need to install on latest main\n",
"!pip uninstall llama-stack\n",
"!pip install git+https://github.com/meta-llama/llama-stack.git@main"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9jJ75JlnETTH",
"outputId": "76bd3912-f814-428c-88e1-c1113af77856"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed handler StreamHandler from root logger\n"
]
}
],
"source": [
"# disable logging for clean server logs\n",
"import logging\n",
"def remove_root_handlers():\n",
" root_logger = logging.getLogger()\n",
" for handler in root_logger.handlers[:]:\n",
" root_logger.removeHandler(handler)\n",
" print(f\"Removed handler {handler.__class__.__name__} from root logger\")\n",
"\n",
"\n",
"remove_root_handlers()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_t_tcWq0JcJ4"
},
"source": [
"##### Building a Search Agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4iCO59kP20Zs",
"outputId": "f6179de6-054d-4452-a893-8d9b64c5a0d1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inference> Let me check the latest sports news.\n",
"inference> bravy_search.call(query=\"Bill Cosby South Park episode\")\n",
"CustomTool> Unknown tool `bravy_search` was called.\n",
"inference> brave_search.call(query=\"Andrew Tate kickboxing name\")\n",
"tool_execution> Tool:brave_search Args:{'query': 'Andrew Tate kickboxing name'}\n",
"tool_execution> Tool:brave_search Response:{\"query\": \"Andrew Tate kickboxing name\", \"top_k\": [{\"title\": \"Andrew Tate kickboxing record: How many championships ... - FirstSportz\", \"url\": \"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\", \"content\": \"Andrew Tate's Kickboxing career. During his kickboxing career, he used the nickname \\\"King Cobra,\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\", \"score\": 0.9996244, \"raw_content\": null}, {\"title\": \"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\", \"url\": \"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\", \"content\": \"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\", \"score\": 0.99909246, \"raw_content\": null}, {\"title\": \"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\", \"url\": \"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\", \"content\": \"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\", \"score\": 0.9976586, \"raw_content\": null}, {\"title\": \"About Andrew Tate: A Journey from Champion to Controversy\", \"url\": \"https://reachmorpheus.com/andrew-tate/\", \"content\": \"Andrew Tate's kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\", \"score\": 0.99701905, \"raw_content\": null}, {\"title\": \"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\", \"url\": \"https://www.nextbiography.com/andrew-tate/\", \"content\": \"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\", \"score\": 0.99368566, \"raw_content\": null}]}\n",
"shield_call> No Violation\n",
"inference> Andrew Tate's kickboxing name is \"King Cobra.\"\n"
]
}
],
"source": [
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from google.colab import userdata\n",
"\n",
"agent_config = AgentConfig(\n",
" model=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
" instructions=\"You are a helpful assistant. Use search tool to answer the questions. \",\n",
" tools=(\n",
" [\n",
" {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
" }\n",
" ]\n",
" ),\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=False,\n",
")\n",
"agent = Agent(client, agent_config)\n",
"user_prompts = [\n",
" \"Which teams played in the NBA western conference finals of 2024\",\n",
" \"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\n",
" \"What is the British-American kickboxer Andrew Tate's kickboxing name?\",\n",
"]\n",
"\n",
"session_id = agent.create_session(\"test-session\")\n",
"\n",
"for prompt in user_prompts:\n",
" response = agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": prompt,\n",
" }\n",
" ],\n",
" session_id=session_id,\n",
" )\n",
"\n",
" for log in EventLogger().log(response):\n",
" log.print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ekOS2kM4P0LM"
},
"source": [
"##### Query Telemetry"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 760
},
"id": "agkWgToGAsuA",
"outputId": "647cd5d2-7610-4fd6-ef66-c3f2f782a1b0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Getting traces for session_id=ac651ce8-2281-47f2-8814-ef947c066e40\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input'</span>: <span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'content: Let me check the latest sports news. tool_calls: []'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input'</span>: <span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='19bd3554-e670-4856-89d0-c63f5b016245', tool_name='bravy_search', arguments={'query': 'Bill Cosby South Park episode'})]\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input'</span>: <span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":{\"query\":\"Bill Cosby South Park episode\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='526045a7-5f51-40fb-ba97-5ad29610e511', tool_name=&lt;BuiltinTool.brave_search: 'brave_search'&gt;, arguments={'query': 'Andrew Tate kickboxing name'})]\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":{\"query\":\"Andrew Tate kickboxing name\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"{\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": [{\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null}]}\"}'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input'</span>: <span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":{\"query\":\"Bill Cosby South Park episode\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":{\"query\":\"Andrew Tate kickboxing name\"}}]}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"{\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": [{\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null}]}\"}'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'output'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'content: Andrew Tate\\'s kickboxing name is \"King Cobra.\" tool_calls: []'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">]</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m'content: Let me check the latest sports news. tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='19bd3554-e670-4856-89d0-c63f5b016245', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m='bravy_search', \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Bill Cosby South Park episode'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Bill Cosby South Park episode\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='526045a7-5f51-40fb-ba97-5ad29610e511', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m=\u001b[0m\u001b[32m<\u001b[0m\u001b[32mBuiltinTool.brave_search:\u001b[0m\u001b[32m 'brave_search'\u001b[0m\u001b[32m>\u001b[0m\u001b[32m, \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Andrew Tate kickboxing name'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Andrew Tate kickboxing name\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": \u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input'\u001b[0m: \u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Bill Cosby South Park episode\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Andrew Tate kickboxing name\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": \u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m'content: Andrew Tate\\'s kickboxing name is \"King Cobra.\" tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(f\"Getting traces for session_id={session_id}\")\n",
"import json\n",
"from rich.pretty import pprint\n",
"\n",
"agent_logs = []\n",
"\n",
"for span in client.telemetry.query_spans(\n",
" attribute_filters=[\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" ],\n",
" attributes_to_return=[\"input\", \"output\"]\n",
" ):\n",
" if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n",
"\n",
"pprint(agent_logs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QF30H7ufP2RE"
},
"source": [
"##### Post-Process Telemetry Results & Evaluate\n",
"\n",
"- Now, we want to run evaluation to assert that our search agent succesfully calls brave_search from online traces.\n",
"- We will first post-process the agent's telemetry logs and run evaluation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 411
},
"id": "sy4Xaff_Avuu",
"outputId": "cb68bae7-b21d-415d-8e71-612bd383c793"
},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input_query'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'content: Let me check the latest sports news. tool_calls: []'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'expected_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'brave_search'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input_query'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='19bd3554-e670-4856-89d0-c63f5b016245', tool_name='bravy_search', arguments={'query': 'Bill Cosby South Park episode'})]\"</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'expected_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'brave_search'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'input_query'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"content: tool_calls: [ToolCall(call_id='526045a7-5f51-40fb-ba97-5ad29610e511', tool_name=&lt;BuiltinTool.brave_search: 'brave_search'&gt;, arguments={'query': 'Andrew Tate kickboxing name'})]\"</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'expected_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'brave_search'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">]</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'content: Let me check the latest sports news. tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='19bd3554-e670-4856-89d0-c63f5b016245', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m='bravy_search', \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Bill Cosby South Park episode'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='526045a7-5f51-40fb-ba97-5ad29610e511', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m=\u001b[0m\u001b[32m<\u001b[0m\u001b[32mBuiltinTool.brave_search:\u001b[0m\u001b[32m 'brave_search'\u001b[0m\u001b[32m>\u001b[0m\u001b[32m, \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Andrew Tate kickboxing name'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringScoreResponse</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">results</span>=<span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'basic::subset_of'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'accuracy'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'accuracy'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3333333333333333</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_correct'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_total'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span><span style=\"font-weight: bold\">}}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>, <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>, <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span><span style=\"font-weight: bold\">}]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;35mScoringScoreResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mresults\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'basic::subset_of'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m0.3333333333333333\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m3\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# post-process telemetry spance and prepare data for eval\n",
"# in this case, we want to assert that all user prompts is followed by a tool call\n",
"import ast\n",
"import json\n",
"\n",
"eval_rows = []\n",
"\n",
"for log in agent_logs:\n",
" last_msg = log['input'][-1]\n",
" if \"\\\"role\\\":\\\"user\\\"\" in last_msg:\n",
" eval_rows.append(\n",
" {\n",
" \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n",
" },\n",
" )\n",
"\n",
"pprint(eval_rows)\n",
"scoring_params = {\n",
" \"basic::subset_of\": None,\n",
"}\n",
"scoring_response = client.scoring.score(input_rows=eval_rows, scoring_functions=scoring_params)\n",
"pprint(scoring_response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VdHk5PmgOl8T"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"bxs0FJ1ckGa6",
"qwXHwHq4lS1s",
"eyziqe_Em6d6",
"1yxrvqi8lhsM"
],
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"017a81d7160240a398947545963856f5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"0218397c573e4b28bfb4ffa66464d50f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"04acde771d0a46699e1de07d9733d1a3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_399a6417b23e4593bb244ec3abb6b46d",
"max": 453677660,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_53a321f36b0d4e08a74a5bcfbd04434b",
"value": 453677660
}
},
"083fd2549abd4b03bd41d8b92ec28f42": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"093bdcb608cf4b4fa37b0032a3915187": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"10c0d50d7c204de0b4c8e8f4d3ec0af5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"117468099dbc42fdaafc08207eaac7ab": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"12c6f1180eeb4e9eb9037ea5dd24ec8e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"158115266c284c4f8dbce3586151cbf1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"179d41b80dc841e8a440482516b8bca5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"1cf8eeb8d81c4e8a8e95dd43296a78b9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"201bd914f9884e46b8e6df9d9900a6e8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"21cf0e35ecd845a8b5e7c5ce241cf177": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"22b1ecd2eff14770bcfb0c62d3d4213f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"24e48376a72940679989a39a40bbe7f6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_484df732051540859bc7ac9cecadc83c",
"IPY_MODEL_4b33b1db50c34a2fa957d81a71a2a47f",
"IPY_MODEL_e51d501e2f994baba40345ad632eabee"
],
"layout": "IPY_MODEL_631a85e420b64e8cb6915af59c5ce08a"
}
},
"25529e7fd57049d2816d31f696eab1fd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"2932b06afde9468a976eb6bfb072b80e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"340fbbb4982c460992c88885e79b47db": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"399a6417b23e4593bb244ec3abb6b46d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3b70fa4e43ef4951862e119378c3c501": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3d0344a9cc744e369da1b6b7ea1b3be8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3e26bc24a3e44b4582f57913bdf98de4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"44f585990aa244d8ba61f892dc1ccc1c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_4fc59928a0544f95a4438b37d19ca437",
"IPY_MODEL_fb644d47049f495397d0e60597c86ea3",
"IPY_MODEL_78632694ff694442bc3fefc2cac2cbf5"
],
"layout": "IPY_MODEL_083fd2549abd4b03bd41d8b92ec28f42"
}
},
"47f876cf41484d55b645e1e99337423a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"484df732051540859bc7ac9cecadc83c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_70af9cb2838c4a92bd67f8cb5c98d97f",
"placeholder": "​",
"style": "IPY_MODEL_158115266c284c4f8dbce3586151cbf1",
"value": "Generating test split: 100%"
}
},
"4b33b1db50c34a2fa957d81a71a2a47f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_ce5019b36cde44c58c5f596dbb59a2f8",
"max": 287,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_b90d660ca8584ba1815a3c66b420c079",
"value": 287
}
},
"4bc266d49a6741a88350e029d101425b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_47f876cf41484d55b645e1e99337423a",
"placeholder": "​",
"style": "IPY_MODEL_340fbbb4982c460992c88885e79b47db",
"value": " 461M/461M [00:11&lt;00:00, 31.2MB/s]"
}
},
"4f788a7920c346f3b42900825bd6711a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_8e9358ec7d474808bb96c13e13489c67",
"IPY_MODEL_f0dfeee2a8d64dedbc8ef55ad4e69932",
"IPY_MODEL_9437b707bf1a4847a50aafeb4252dab5"
],
"layout": "IPY_MODEL_f255707788704a76bd1651f26a22402d"
}
},
"4fc59928a0544f95a4438b37d19ca437": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_611d6472a58d419583acc416767a4c90",
"placeholder": "​",
"style": "IPY_MODEL_98c5ce434cff454eaaa3f0fd3498183a",
"value": "validation-00000-of-00001.parquet: 100%"
}
},
"4fed5720f30b4b3cbbc606a4f25e223b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_6fa866b9971542739b0ed26d90ceac80",
"IPY_MODEL_fe7553b513954cc68c427b5d9d260b33",
"IPY_MODEL_4bc266d49a6741a88350e029d101425b"
],
"layout": "IPY_MODEL_da57445f98e7427589962836c2b4287e"
}
},
"4ff3a6aaf706460bbba01b248b93000e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"500a072c09da41759cb2c942a16d8429": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_e6d6e516cd03452297d80c36376855dd",
"max": 29453850,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_6ae0fadb3aeb4be18a9ab3279fb23145",
"value": 29453850
}
},
"52150fd494d24eea89b5232077509355": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_b09b2690894749339a9172e5ad0a9b75",
"placeholder": "​",
"style": "IPY_MODEL_cbed38801163438d891879b756f5baab",
"value": "test-00001-of-00003.parquet: 100%"
}
},
"53a321f36b0d4e08a74a5bcfbd04434b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"5b0b5a3f79e94c51aae48fe0dd34ba0e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"611d6472a58d419583acc416767a4c90": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"626ef2f811ae4e119a0e85cebe92b91d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"631a85e420b64e8cb6915af59c5ce08a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"6ae0fadb3aeb4be18a9ab3279fb23145": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"6c0a6a7fa8ca4e1c961a36305f0e7638": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"6fa866b9971542739b0ed26d90ceac80": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_ad1fb86cc1f94fd9911eda03cf4a3783",
"placeholder": "​",
"style": "IPY_MODEL_fdefb51ad4c4418b98c5826126558011",
"value": "test-00000-of-00003.parquet: 100%"
}
},
"70af9cb2838c4a92bd67f8cb5c98d97f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"737116977f474ec0b68d88a40fd1086c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"74b58e4647644c9daf9af488942fdaf4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_25529e7fd57049d2816d31f696eab1fd",
"placeholder": "​",
"style": "IPY_MODEL_093bdcb608cf4b4fa37b0032a3915187",
"value": " 36.0k/36.0k [00:00&lt;00:00, 1.29MB/s]"
}
},
"75f06408071c494f934bb909b84110d1": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"78632694ff694442bc3fefc2cac2cbf5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_0218397c573e4b28bfb4ffa66464d50f",
"placeholder": "​",
"style": "IPY_MODEL_9b01bcd6e5174be2af19f457047017c8",
"value": " 165M/165M [00:03&lt;00:00, 42.9MB/s]"
}
},
"78a2d2d4ee3f42f3be42ef4baa298561": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_cab80632b7564a9eb59583e09573c1ee",
"placeholder": "​",
"style": "IPY_MODEL_10c0d50d7c204de0b4c8e8f4d3ec0af5",
"value": "README.md: 100%"
}
},
"78d0e2aa93674bbeb42bff87a23cce9b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"7b98103300814f3caea84266263b95a2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_b8c0c8aaac0d4032bf5c673a43d084ab",
"placeholder": "​",
"style": "IPY_MODEL_d1f32499fa3f4795b92361637e23a9bb",
"value": " 454M/454M [00:11&lt;00:00, 40.4MB/s]"
}
},
"7c4d1de626784a59a7e0a33c24086186": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"82991dcc80f14af9bd2e95f705980676": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_e17d286a965a49cfb8d5bf885865cb1e",
"placeholder": "​",
"style": "IPY_MODEL_ca015c1a0c1449e68edb282462435a3f",
"value": "test-00002-of-00003.parquet: 100%"
}
},
"84570fe2c2a54a068fb9b8cbc8b041a1": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"8e9358ec7d474808bb96c13e13489c67": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_3b70fa4e43ef4951862e119378c3c501",
"placeholder": "​",
"style": "IPY_MODEL_6c0a6a7fa8ca4e1c961a36305f0e7638",
"value": "Generating dev split: 100%"
}
},
"93ee645d54f34acdb0d15092d4a6f0d1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_4ff3a6aaf706460bbba01b248b93000e",
"placeholder": "​",
"style": "IPY_MODEL_bfd75a39f0154c30adbaad1e2ca0f1e2",
"value": " 471M/471M [00:11&lt;00:00, 41.5MB/s]"
}
},
"9437b707bf1a4847a50aafeb4252dab5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_d2029292327b488db02fd123ee2b75af",
"placeholder": "​",
"style": "IPY_MODEL_3e26bc24a3e44b4582f57913bdf98de4",
"value": " 5/5 [00:00&lt;00:00,  8.03 examples/s]"
}
},
"963cf422ca894d82b0dd94c6165d41bf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_f5b34a743ce54fb591f25b04a2651d65",
"placeholder": "​",
"style": "IPY_MODEL_dec6399e2c5341aead66e1674d3e6c72",
"value": " 30/30 [00:03&lt;00:00,  8.23 examples/s]"
}
},
"9659140487ca4d3ea799196d2c1ecf61": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_52150fd494d24eea89b5232077509355",
"IPY_MODEL_04acde771d0a46699e1de07d9733d1a3",
"IPY_MODEL_7b98103300814f3caea84266263b95a2"
],
"layout": "IPY_MODEL_75f06408071c494f934bb909b84110d1"
}
},
"9785009392934e3bbb229e8781667cbc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_fa4800a506ac480984d58933580df086",
"placeholder": "​",
"style": "IPY_MODEL_117468099dbc42fdaafc08207eaac7ab",
"value": " 29.5M/29.5M [00:00&lt;00:00, 36.5MB/s]"
}
},
"98c5ce434cff454eaaa3f0fd3498183a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"9b01bcd6e5174be2af19f457047017c8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"9d2b6eabf7e14436b72bbf374b4a2a0a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_b5d7cb5a6157449a850ef0e12e3d3eb7",
"IPY_MODEL_c245d316bf9e44dabe5bfd1e47fc8d2e",
"IPY_MODEL_963cf422ca894d82b0dd94c6165d41bf"
],
"layout": "IPY_MODEL_78d0e2aa93674bbeb42bff87a23cce9b"
}
},
"ad1fb86cc1f94fd9911eda03cf4a3783": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"aef4172d916f40b0ab4ed09104e10f24": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"b09b2690894749339a9172e5ad0a9b75": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"b5d7cb5a6157449a850ef0e12e3d3eb7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_12c6f1180eeb4e9eb9037ea5dd24ec8e",
"placeholder": "​",
"style": "IPY_MODEL_017a81d7160240a398947545963856f5",
"value": "Generating validation split: 100%"
}
},
"b77fe05bbcf84cdc8ef85b264ccd35f6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"b8c0c8aaac0d4032bf5c673a43d084ab": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"b90d660ca8584ba1815a3c66b420c079": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"ba5e6ca09f174ef3a348453cf5cfc24a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_626ef2f811ae4e119a0e85cebe92b91d",
"max": 36030,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_aef4172d916f40b0ab4ed09104e10f24",
"value": 36030
}
},
"bfd75a39f0154c30adbaad1e2ca0f1e2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"c06f9a090fb54c74b947634bf6d11fa8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_82991dcc80f14af9bd2e95f705980676",
"IPY_MODEL_cd832e3842b945aabbb327856053f261",
"IPY_MODEL_93ee645d54f34acdb0d15092d4a6f0d1"
],
"layout": "IPY_MODEL_b77fe05bbcf84cdc8ef85b264ccd35f6"
}
},
"c245d316bf9e44dabe5bfd1e47fc8d2e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_1cf8eeb8d81c4e8a8e95dd43296a78b9",
"max": 30,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_5b0b5a3f79e94c51aae48fe0dd34ba0e",
"value": 30
}
},
"c452ccbf47a44073aee710175f707a7d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"c788d4e9e1e24dca9b6503689df9b631": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_d1587e2144bf46299c1bdec3ea96e4e7",
"IPY_MODEL_500a072c09da41759cb2c942a16d8429",
"IPY_MODEL_9785009392934e3bbb229e8781667cbc"
],
"layout": "IPY_MODEL_84570fe2c2a54a068fb9b8cbc8b041a1"
}
},
"ca015c1a0c1449e68edb282462435a3f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"cab80632b7564a9eb59583e09573c1ee": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"cbed38801163438d891879b756f5baab": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"cd832e3842b945aabbb327856053f261": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_2932b06afde9468a976eb6bfb072b80e",
"max": 470745176,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_d027c807ddc04f89bec41dc05fde7718",
"value": 470745176
}
},
"ce5019b36cde44c58c5f596dbb59a2f8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"d027c807ddc04f89bec41dc05fde7718": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"d1587e2144bf46299c1bdec3ea96e4e7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_f9e579c58e3f4ae0bbb721dffa33bf0a",
"placeholder": "​",
"style": "IPY_MODEL_737116977f474ec0b68d88a40fd1086c",
"value": "dev-00000-of-00001.parquet: 100%"
}
},
"d1f32499fa3f4795b92361637e23a9bb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"d2029292327b488db02fd123ee2b75af": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"d56e218958a041e286e80f24e400ab0b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"da57445f98e7427589962836c2b4287e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"dec6399e2c5341aead66e1674d3e6c72": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"e17d286a965a49cfb8d5bf885865cb1e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"e51d501e2f994baba40345ad632eabee": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_7c4d1de626784a59a7e0a33c24086186",
"placeholder": "​",
"style": "IPY_MODEL_21cf0e35ecd845a8b5e7c5ce241cf177",
"value": " 287/287 [00:23&lt;00:00, 12.48 examples/s]"
}
},
"e6d6e516cd03452297d80c36376855dd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"f0dfeee2a8d64dedbc8ef55ad4e69932": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_201bd914f9884e46b8e6df9d9900a6e8",
"max": 5,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_f53b7ada01084e73bba6e14a95e2a534",
"value": 5
}
},
"f255707788704a76bd1651f26a22402d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"f53b7ada01084e73bba6e14a95e2a534": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"f5b34a743ce54fb591f25b04a2651d65": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"f9e579c58e3f4ae0bbb721dffa33bf0a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"fa4800a506ac480984d58933580df086": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"fb644d47049f495397d0e60597c86ea3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_3d0344a9cc744e369da1b6b7ea1b3be8",
"max": 165333397,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_c452ccbf47a44073aee710175f707a7d",
"value": 165333397
}
},
"fdefb51ad4c4418b98c5826126558011": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"fe7553b513954cc68c427b5d9d260b33": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_179d41b80dc841e8a440482516b8bca5",
"max": 461411018,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_22b1ecd2eff14770bcfb0c62d3d4213f",
"value": 461411018
}
},
"feb82e061ee44283b4a46be858ef4cd7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_78a2d2d4ee3f42f3be42ef4baa298561",
"IPY_MODEL_ba5e6ca09f174ef3a348453cf5cfc24a",
"IPY_MODEL_74b58e4647644c9daf9af488942fdaf4"
],
"layout": "IPY_MODEL_d56e218958a041e286e80f24e400ab0b"
}
}
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Reference in a new issue View git blame Copy permalink