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Add chapter 10 #549

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65 changes: 65 additions & 0 deletions course/fr/chapter10/section2.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "kK8kfIfqrgrc"
},
"source": [
"# Configurez votre instance Argilla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cGOYFrYyrgrd"
},
"outputs": [],
"source": [
"!pip install argilla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mWB23rrFrgrf"
},
"outputs": [],
"source": [
"import argilla as rg\n",
"\n",
"HF_TOKEN = \"...\" # uniquement pour les spaces privés\n",
"\n",
"client = rg.Argilla(\n",
" api_url=\"...\",\n",
" api_key=\"...\",\n",
" headers={\"Authorization\": f\"Bearer {HF_TOKEN}\"}, # uniquement pour les spaces privés\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UVHJPCFKrgrg"
},
"outputs": [],
"source": [
"client.me"
]
}
],
"metadata": {
"colab": {
"name": "Set up your Argilla instance",
"provenance": []
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
115 changes: 115 additions & 0 deletions course/fr/chapter10/section3.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chargez votre jeu de données dans Argilla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install argilla datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import argilla as rg\n",
"\n",
"HF_TOKEN = \"...\" # uniquement pour les spaces privés\n",
"\n",
"client = rg.Argilla(\n",
" api_url=\"...\",\n",
" api_key=\"...\",\n",
" headers={\"Authorization\": f\"Bearer {HF_TOKEN}\"}, # uniquement pour les spaces privés\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': Value(dtype='string', id=None),\n",
" 'label': Value(dtype='int64', id=None),\n",
" 'label_text': Value(dtype='string', id=None)}"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"data = load_dataset(\"SetFit/ag_news\", split=\"train\")\n",
"data.features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"settings = rg.Settings(\n",
" fields=[rg.TextField(name=\"text\")],\n",
" questions=[\n",
" rg.LabelQuestion(\n",
" name=\"label\", title=\"Classifier le texte :\", labels=data.unique(\"label_text\")\n",
" ),\n",
" rg.SpanQuestion(\n",
" name=\"entities\",\n",
" title=\"Surligner toutes les entités présentes dans le texte :\",\n",
" labels=[\"PERSON\", \"ORG\", \"LOC\", \"EVENT\"],\n",
" field=\"text\",\n",
" ),\n",
" ],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = rg.Dataset(name=\"ag_news\", settings=settings)\n",
"\n",
"dataset.create()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.records.log(data, mapping={\"label_text\": \"label\"})"
]
}
],
"metadata": {
"colab": {
"name": "Load your dataset to Argilla",
"provenance": []
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
95 changes: 95 additions & 0 deletions course/fr/chapter10/section5.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Utilisez votre jeu de données annoté"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install argilla"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import argilla as rg\n",
"\n",
"HF_TOKEN = \"...\" # uniquement pour les spaces privés\n",
"\n",
"client = rg.Argilla(\n",
" api_url=\"...\",\n",
" api_key=\"...\",\n",
" headers={\"Authorization\": f\"Bearer {HF_TOKEN}\"}, # uniquement pour les spaces privés\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = client.datasets(name=\"ag_news\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"status_filter = rg.Query(filter=rg.Filter([(\"status\", \"==\", \"completed\")]))\n",
"\n",
"filtered_records = dataset.records(status_filter)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"filtered_records.to_datasets().push_to_hub(\"argilla/ag_news_annotated\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.to_hub(repo_id=\"argilla/ag_news_annotated\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = rg.Dataset.from_hub(repo_id=\"argilla/ag_news_annotated\")"
]
}
],
"metadata": {
"colab": {
"name": "Use your annotated dataset",
"provenance": []
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 4
}