7. Applying BERT for other languages/7.07. Getting representation of French sentence with FlauBERT .ipynb

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    "# Getting representation of French sentence with FlauBERT \n",
    "Let's explore how to use the pre-trained FlauBERT for obtaining the French sentence representation. First, let us import FlaubertTokenizer, FlaubertModel from transformers and also the torch library:\n"
   ]
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    "!pip install transformers==3.5.1"
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   "source": [
    "from transformers import FlaubertTokenizer, FlaubertModel\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6A2I9udZE0XL"
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   "source": [
    "\n",
    "Let's download and load the pre-trained flaubert_base_cased model: "
   ]
  },
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    "model = FlaubertModel.from_pretrained('flaubert/flaubert_base_cased')"
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    "Next, we download and load the tokenizer which is used for pre-training the  flaubert_base_cased model: "
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   "source": [
    "tokenizer = FlaubertTokenizer.from_pretrained('flaubert/flaubert_base_cased')"
   ]
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   "source": [
    "\n",
    "\n",
    "Now, let us define the input sentence for which we want to compute the embedding: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
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   "source": [
    "sentence = \"Paris est ma ville préférée\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g3q0kVdDE0XT"
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   "source": [
    "\n",
    "Next, we tokenize the sentence and get the token ids:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "id": "NrTK-HQPE0XU"
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   "outputs": [],
   "source": [
    "token_ids = tokenizer.encode(sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "\n",
    "Convert the tokens ids to torch tensors: "
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "token_ids = torch.tensor(token_ids).unsqueeze(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xlwhyvHAE0XW"
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   "source": [
    "\n",
    "Obtain the representation of each token in our sentence using the pre-trained flaubert_base_casedmodel:\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "source": [
    "representation = model(token_ids)[0]"
   ]
  },
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   "source": [
    "\n",
    "\n",
    "Let us check the shape of representation:"
   ]
  },
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   "execution_count": 9,
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   "outputs": [
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       "torch.Size([1, 7, 768])"
      ]
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     "execution_count": 9,
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   "source": [
    "representation.shape"
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  },
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   "source": [
    "\n",
    "As we can observe our representation size is [1, 7, 768] including the [CLS] and [SEP] tokens. We learned that the [CLS] token holds the aggregate representation of a sentence, so we get the representation of the [CLS] token and use it as the sentence representation: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
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   "source": [
    "cls_rep = representation[:, 0, :]"
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   "source": [
    "\n",
    "\n",
    "That's it. In this way, we use the pre-trained FlauBERT and obtain the representation for French text. We can also use the pre-trained FlauBERT and fine-tune it for any downstream tasks. "
   ]
  }
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