5-putting-it-all-together.ipynb

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   "source": [
    "# Putting it all together"
   ]
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   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "\n",
    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
    "\n",
    "model_inputs = tokenizer(sequence)"
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    "model_inputs"
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    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
    "\n",
    "model_inputs = tokenizer(sequence)\n",
    "model_inputs"
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       "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], [101, 2061, 2031, 1045, 999, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]}"
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   "source": [
    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
    "\n",
    "model_inputs = tokenizer(sequences)\n",
    "model_inputs"
   ]
  },
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   "execution_count": 5,
   "id": "7ed4caa9",
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   "outputs": [],
   "source": [
    "# Will pad the sequences up to the maximum sequence length\n",
    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
    "\n",
    "# Will pad the sequences up to the model max length\n",
    "# (512 for BERT or DistilBERT)\n",
    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
    "\n",
    "# Will pad the sequences up to the specified max length\n",
    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
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   },
   "source": [
    "## Special tokens"
   ]
  },
  {
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     "text": [
      "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
      "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]\n"
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   "source": [
    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\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)"
   ]
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