course/fr/chapter2/section6_tf.ipynb

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   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Tout assembler (TensorFlow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8rD9hZss65OJ"
   },
   "source": [
    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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    },
    "id": "-EeZ0gn-65OK",
    "outputId": "077f4f51-17b5-4e34-c892-ee6f9e8cd930"
   },
   "outputs": [],
   "source": [
    "!pip install transformers[sentencepiece]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "04crq_ak65OL"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "checkpoint = \"tblard/tf-allocine\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "\n",
    "sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n",
    "\n",
    "model_inputs = tokenizer(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "do8HV6mc65ON"
   },
   "outputs": [],
   "source": [
    "sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n",
    "\n",
    "model_inputs = tokenizer(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0KnRX6fD65OO"
   },
   "outputs": [],
   "source": [
    "sequences = [\n",
    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
    "    \"Moi aussi !\",\n",
    "]\n",
    "\n",
    "model_inputs = tokenizer(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TGIPZ3C565OP"
   },
   "outputs": [],
   "source": [
    "# Remplit les séquences jusqu'à la longueur maximale de la séquence\n",
    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
    "\n",
    "# Remplit les séquences jusqu'à la longueur maximale du modèle (512 pour BERT ou DistilBERT)\n",
    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
    "\n",
    "# Remplit les séquences jusqu'à la longueur maximale spécifiée\n",
    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6oyPdoLa65OQ"
   },
   "outputs": [],
   "source": [
    "sequences = [\n",
    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
    "    \"Moi aussi !\",\n",
    "]\n",
    "\n",
    "# Tronque les séquences qui sont plus longues que la longueur maximale du modèle (512 pour BERT ou DistilBERT)\n",
    "model_inputs = tokenizer(sequences, truncation=True)\n",
    "\n",
    "# Tronque les séquences qui sont plus longues que la longueur maximale spécifiée\n",
    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SjXOriAe65OR"
   },
   "outputs": [],
   "source": [
    "sequences = [\n",
    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
    "    \"Moi aussi !\",\n",
    "]\n",
    "\n",
    "# Retourne des tenseurs PyTorch\n",
    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
    "\n",
    "# Retourne des tenseurs TensorFlow\n",
    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
    "\n",
    "# Retourne des tableaux NumPy\n",
    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wal0cML465OT",
    "outputId": "56276f8a-ca78-43e4-e9f7-1d93dfdf52fc"
   },
   "outputs": [],
   "source": [
    "sequence = \"J'ai attendu un cours de HuggingFace toute ma vie.\"\n",
    "\n",
    "model_inputs = tokenizer(sequence)\n",
    "print(model_inputs[\"input_ids\"])\n",
    "\n",
    "tokens = tokenizer.tokenize(sequence)\n",
    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
    "print(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "37Uboygp65OU",
    "outputId": "8aaaa410-d725-47e0-bfea-649ec147b5bf"
   },
   "outputs": [],
   "source": [
    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
    "print(tokenizer.decode(ids))"
   ]
  },
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   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
    "\n",
    "checkpoint = \"tblard/tf-allocine\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
    "sequences = [\n",
    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
    "    \"Moi aussi !\",\n",
    "]\n",
    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
    "output = model(**tokens)"
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