examples/Sequence Labeling: Pretrained BERT.ipynb

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     "text": [
      "Number of training examples: 3131\n",
      "Number of testing examples: 783\n",
      "Unique tokens in TEXT vocabulary: 5444\n",
      "Unique tokens in TAGS vocabulary: 13\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "\n",
    "from xtagger import xtagger_dataset_to_df\n",
    "from xtagger import df_to_torchtext_data\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "nltk_data = list(nltk.corpus.treebank.tagged_sents(tagset='universal'))\n",
    "train_set,test_set =train_test_split(nltk_data,train_size=0.8,test_size=0.2,random_state = 2112)\n",
    "\n",
    "df_train = xtagger_dataset_to_df(train_set)\n",
    "df_test = xtagger_dataset_to_df(test_set)\n",
    "\n",
    "device = torch.device(\"cuda\")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
    "\n",
    "train_iterator, valid_iterator, test_iterator, TEXT, TAGS = df_to_torchtext_data(\n",
    "    df_train, \n",
    "    df_test, \n",
    "    device, \n",
    "    transformers = True,\n",
    "    tokenizer = tokenizer,\n",
    "    batch_size=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    },
    "id": "orAhtoENM0N3",
    "outputId": "6bedad4c-94d6-4e6f-f854-aeed30659aab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 9997 trainable parameters\n",
      "The model has 108310272 non-trainable parameters\n"
     ]
    }
   ],
   "source": [
    "from xtagger import BERTForTagging\n",
    "\n",
    "model = BERTForTagging(\n",
    "    output_dim = 13,\n",
    "    TEXT = TEXT,\n",
    "    TAGS = TAGS,\n",
    "    dropout = 0.2,\n",
    "    device = device,\n",
    "    cuda = True\n",
    "  )"
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      "Evaluating...\n"
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      "Evaluating...\n"
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      "Evaluating...\n"
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      "{'eval': {'acc': 88.96136477671851, 'avg_f1': {'weighted': 88.84846805573173, 'micro': 88.96136477671851, 'macro': 80.7721702756681}}, 'train': {'acc': 89.5970079013202, 'avg_f1': {'weighted': 89.53644383963858, 'micro': 89.5970079013202, 'macro': 81.46808716754805}}, 'eval_loss': 0.36782260656356813, 'train_loss': 0.35705909984452383}\n",
      "Evaluating...\n"
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      "Evaluating...\n"
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      "{'eval': {'acc': 89.33266432513798, 'avg_f1': {'weighted': 89.17648653578746, 'micro': 89.33266432513798, 'macro': 81.155844533212}}, 'train': {'acc': 90.00693532806578, 'avg_f1': {'weighted': 89.91018870940354, 'micro': 90.00693532806578, 'macro': 81.92647391325453}}, 'eval_loss': 0.34750911593437195, 'train_loss': 0.33492926401751383}\n",
      "Evaluating...\n"
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      "{'eval': {'acc': 89.50827897641747, 'avg_f1': {'weighted': 89.34650556168636, 'micro': 89.50827897641747, 'macro': 81.3793538412704}}, 'train': {'acc': 90.22118742724098, 'avg_f1': {'weighted': 90.11575875347711, 'micro': 90.22118742724096, 'macro': 82.22700783168506}}, 'eval_loss': 0.3388308537006378, 'train_loss': 0.326385920905337}\n"
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    "model.fit(\n",
    "    train_iterator, \n",
    "    valid_iterator, \n",
    "    eval_metrics = [\"acc\", \"avg_f1\"], \n",
    "    epochs = 10\n",
    ")"
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       "  0%|          | 0/25 [00:00<?, ?it/s]"
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       "([('there', 'DET'),\n",
       "  ('are', 'VERB'),\n",
       "  ('no', 'ADV'),\n",
       "  ('two', 'NUM'),\n",
       "  ('words', 'NOUN'),\n",
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