{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using pretrained models (TensorFlow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install datasets evaluate transformers[sentencepiece]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\n",
       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
       "]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
    "\n",
    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Using pretrained models (TensorFlow)",
   "provenance": []
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}