notebooks/model_view_bart.ipynb

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   "source": [
    "from transformers import AutoTokenizer, AutoModel, utils\n",
    "from bertviz import model_view\n",
    "\n",
    "utils.logging.set_verbosity_error()  # Remove line to see warnings\n",
    "\n",
    "# Initialize tokenizer and model. Be sure to set output_attentions=True.\n",
    "# Load BART fine-tuned for summarization on CNN/Daily Mail dataset\n",
    "model_name = \"facebook/bart-large-cnn\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModel.from_pretrained(model_name, output_attentions=True)\n",
    "\n",
    "# get encoded input vectors\n",
    "encoder_input_ids = tokenizer(\"The House Budget Committee voted Saturday to pass a $3.5 trillion spending bill\", return_tensors=\"pt\", add_special_tokens=True).input_ids\n",
    "\n",
    "# create ids of encoded input vectors\n",
    "decoder_input_ids = tokenizer(\"The House Budget Committee passed a spending bill.\", return_tensors=\"pt\", add_special_tokens=True).input_ids\n",
    "\n",
    "outputs = model(input_ids=encoder_input_ids, decoder_input_ids=decoder_input_ids)\n",
    "\n",
    "encoder_text = tokenizer.convert_ids_to_tokens(encoder_input_ids[0])\n",
    "decoder_text = tokenizer.convert_ids_to_tokens(decoder_input_ids[0])\n",
    "\n",
    "model_view(\n",
    "    encoder_attention=outputs.encoder_attentions,\n",
    "    decoder_attention=outputs.decoder_attentions,\n",
    "    cross_attention=outputs.cross_attentions,\n",
    "    encoder_tokens= encoder_text,\n",
    "    decoder_tokens=decoder_text\n",
    ")"
   ]
  }
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