Tarea_2-BERT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gaz6BhNElERZ"
   },
   "source": [
    "# Assignment 2 - Named Entity Recognition (NER) in Spanish"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "52zuHFKwlWjS"
   },
   "source": [
    "- **Nombre:** Mario Vicuña y Miguel Videla\n",
    "\n",
    "- **Usuario o nombre de equipo en Codalab:** TeamChalla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "7NEkU8INCNYm",
    "outputId": "7e7a209c-c061-4f70-e682-8144f5ebf580"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K     |████████████████████████████████| 778kB 9.6MB/s \n",
      "\u001b[K     |████████████████████████████████| 1.1MB 35.1MB/s \n",
      "\u001b[K     |████████████████████████████████| 3.0MB 56.1MB/s \n",
      "\u001b[K     |████████████████████████████████| 890kB 52.2MB/s \n",
      "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[K     |████████████████████████████████| 71kB 4.5MB/s \n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install transformers -q\n",
    "!pip3 install --upgrade torchtext -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "MbmNiwUoCHTa",
    "outputId": "7cd8f82e-e656-4863-ee47-f4e16a9b79a1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Tesla P100-PCIE-16GB'"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import torch\n",
    "import warnings\n",
    "import sklearn.exceptions\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "from torchtext import data, datasets\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
    "from transformers import BertTokenizer, BertForTokenClassification, AdamW, get_linear_schedule_with_warmup\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score\n",
    "\n",
    "SEED = 123\n",
    "torch.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "warnings.filterwarnings(\"ignore\", category=sklearn.exceptions.UndefinedMetricWarning)\n",
    "torch.cuda.get_device_name(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cjsDE4SEDBha"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!wget https://github.com/dccuchile/CC6205/releases/download/Data/train_NER_esp.txt -nc \n",
    "!wget https://github.com/dccuchile/CC6205/releases/download/Data/val_NER_esp.txt -nc \n",
    "!wget https://github.com/dccuchile/CC6205/releases/download/Data/test_NER_esp.txt -nc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NvNmnli_Jvit"
   },
   "outputs": [],
   "source": [
    "# Utils\n",
    "def tokenize_and_preserve_labels(sentence, text_labels, tokenizer):\n",
    "    tokenized_sentence = []\n",
    "    labels = []\n",
    "    for word, label in zip(sentence, text_labels):\n",
    "        tokenized_word = tokenizer.tokenize(word)\n",
    "        n_subwords = len(tokenized_word)\n",
    "        tokenized_sentence.extend(tokenized_word)\n",
    "        labels.extend([label])\n",
    "        if n_subwords > 1:\n",
    "            labels.extend([\"<sub>\"] * (n_subwords - 1))\n",
    "    return tokenized_sentence, labels\n",
    "\n",
    "def ner_preprocess(text, ner_tags, tokenizer, max_len=100, batch_size=32, is_train=True):\n",
    "    tokenized_texts_and_labels = [\n",
    "        tokenize_and_preserve_labels(sent, labs, tokenizer)\n",
    "        for sent, labs in zip(text, ner_tags)\n",
    "    ]\n",
    "    tokenized_texts = [[\"[CLS]\"] + token_label_pair[0] + [\"[SEP]\"] for token_label_pair in tokenized_texts_and_labels]\n",
    "    labels = [[\"O\"] + token_label_pair[1] + [\"O\"] for token_label_pair in tokenized_texts_and_labels]\n",
    "    input_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],\n",
    "                          maxlen=max_len, dtype=\"long\", value=tokenizer.vocab[\"[PAD]\"],\n",
    "                          truncating=\"post\", padding=\"post\")\n",
    "    tags = pad_sequences([[tag2idx.get(l) for l in lab] for lab in labels],\n",
    "                        maxlen=max_len, value=tag2idx[\"<pad>\"], padding=\"post\",\n",
    "                        dtype=\"long\", truncating=\"post\")\n",
    "    attention_masks = [[float(i != tokenizer.vocab[\"[PAD]\"]) for i in ii] for ii in input_ids]\n",
    "    pad_tok = tokenizer.vocab[\"[PAD]\"]\n",
    "    sep_tok = tokenizer.vocab[\"[SEP]\"]\n",
    "    o_lab = tag2idx[\"O\"]\n",
    "    for voc_ids, tag_ids in zip(input_ids, tags):\n",
    "        if voc_ids[-1] == pad_tok:\n",
    "            continue\n",
    "        else:\n",
    "            voc_ids[-1] = sep_tok\n",
    "            tag_ids[-1] = o_lab\n",
    "    tr_inputs = torch.tensor(input_ids)\n",
    "    tr_tags = torch.tensor(tags)\n",
    "    tr_masks = torch.tensor(attention_masks)\n",
    "    train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)\n",
    "    if is_train:\n",
    "        train_sampler = RandomSampler(train_data)\n",
    "    else:\n",
    "        train_sampler = SequentialSampler(train_data)\n",
    "    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
    "    return train_dataloader\n",
    "\n",
    "def ner_loss(logits, labels, attention_mask, sub_idx, num_labels):\n",
    "    loss_fct = CrossEntropyLoss()\n",
    "    active_loss_subwords = labels.view(-1) != sub_idx\n",
    "    active_loss_mask = attention_mask.view(-1) == 1\n",
    "    active_loss = active_loss_mask & active_loss_subwords\n",
    "    active_logits = logits.view(-1, num_labels)\n",
    "    active_labels = torch.where(\n",
    "        active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)\n",
    "    )\n",
    "    loss = loss_fct(active_logits, active_labels)\n",
    "    return loss\n",
    "\n",
    "def calculate_metrics(preds, y_true, inputs, o_idx=0, pad_idx=9, sub_idx=10):\n",
    "    y_pred = preds.argmax(axis=2).flatten()\n",
    "    y_true = y_true.flatten()\n",
    "    mask = (y_true != o_idx) & (y_true != pad_idx) & (y_true != sub_idx)\n",
    "    y_pred = y_pred[mask]\n",
    "    y_true = y_true[mask]\n",
    "    f1 = f1_score(y_true, y_pred, average='macro')\n",
    "    precision = precision_score(y_true, y_pred, average='macro')\n",
    "    recall = recall_score(y_true, y_pred, average='macro')\n",
    "    return precision, recall, f1\n",
    "\n",
    "def set_unk(data):\n",
    "  for i in range(len(data)):\n",
    "    for j in range(len(data[i])):\n",
    "      if data[i][j] == \"<unk>\":\n",
    "        data[i][j] = \"[UNK]\"\n",
    "  return data"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "{'<pad>': 9,\n",
       " '<sub>': 10,\n",
       " 'B-LOC': 3,\n",
       " 'B-MISC': 7,\n",
       " 'B-ORG': 1,\n",
       " 'B-PER': 4,\n",
       " 'I-LOC': 8,\n",
       " 'I-MISC': 6,\n",
       " 'I-ORG': 2,\n",
       " 'I-PER': 5,\n",
       " 'O': 0}"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
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    }
   ],
   "source": [
    "# Data Load\n",
    "TEXT = data.Field(lower=False) \n",
    "NER_TAGS = data.Field(unk_token=None)\n",
    "fields = ((\"text\", TEXT), (\"nertags\", NER_TAGS))\n",
    "\n",
    "train_data, valid_data, test_data = datasets.SequenceTaggingDataset.splits(\n",
    "    path=\"./\",\n",
    "    train=\"train_NER_esp.txt\",\n",
    "    validation=\"val_NER_esp.txt\",\n",
    "    test=\"test_NER_esp.txt\",\n",
    "    fields=fields,\n",
    "    encoding=\"iso-8859-1\",\n",
    "    separator=\" \"\n",
    ")\n",
    "\n",
    "tag_values = ['O', 'B-ORG', 'I-ORG', 'B-LOC', 'B-PER', 'I-PER', 'I-MISC', 'B-MISC', 'I-LOC', '<pad>',  '<sub>']\n",
    "tag2idx = {t: i for i, t in enumerate(tag_values)}\n",
    "tag2idx"
   ]
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   "source": [
    "# Data Preprocessing\n",
    "MAX_LEN = 512\n",
    "BATCH_SIZE = 16\n",
    "\n",
    "inputs_train = set_unk([example.text for example in train_data.examples])\n",
    "labels_train = [example.nertags for example in train_data.examples]\n",
    "inputs_val = set_unk([example.text for example in valid_data.examples])\n",
    "labels_val = [example.nertags for example in valid_data.examples]\n",
    "inputs_test = set_unk([example.text for example in test_data.examples])\n",
    "labels_test = [example.nertags for example in test_data.examples]\n",
    "\n",
    "bert_tokenizer = BertTokenizer.from_pretrained('dccuchile/bert-base-spanish-wwm-cased', do_lower_case=False)\n",
    "#train_dataloader = ner_preprocess(inputs_train, labels_train, tokenizer=bert_tokenizer, max_len=MAX_LEN, batch_size=BATCH_SIZE)\n",
    "train_dataloader = ner_preprocess(inputs_train + inputs_val, labels_train + labels_val, tokenizer=bert_tokenizer, max_len=MAX_LEN, batch_size=BATCH_SIZE)\n",
    "#valid_dataloader = ner_preprocess(inputs_val, labels_val, tokenizer=bert_tokenizer, max_len=MAX_LEN, batch_size=BATCH_SIZE, is_train=False)"
   ]
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     "text": [
      "Some weights of the model checkpoint at dccuchile/bert-base-spanish-wwm-cased were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n",
      "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForTokenClassification were not initialized from the model checkpoint at dccuchile/bert-base-spanish-wwm-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# Model\n",
    "model = BertForTokenClassification.from_pretrained(\n",
    "    \"dccuchile/bert-base-spanish-wwm-cased\",\n",
    "    num_labels=len(tag2idx) - 2,\n",
    "    output_attentions=False,\n",
    "    output_hidden_states=False\n",
    ")\n",
    "model.cuda()\n",
    "\n",
    "FULL_FINETUNING = True\n",
    "if FULL_FINETUNING:\n",
    "    param_optimizer = list(model.named_parameters())\n",
    "    no_decay = ['bias', 'gamma', 'beta']\n",
    "    optimizer_grouped_parameters = [\n",
    "        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],\n",
    "         'weight_decay_rate': 0.01},\n",
    "        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],\n",
    "         'weight_decay_rate': 0.0}\n",
    "    ]\n",
    "else:\n",
    "    param_optimizer = list(model.classifier.named_parameters())\n",
    "    optimizer_grouped_parameters = [{\"params\": [p for n, p in param_optimizer]}]\n",
    "\n",
    "optimizer = AdamW(\n",
    "    optimizer_grouped_parameters,\n",
    "    lr=3e-5,\n",
    "    eps=1e-8\n",
    ")\n",
    "\n",
    "epochs = 10\n",
    "max_grad_norm = 1.0\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    num_warmup_steps=0,\n",
    "    num_training_steps=total_steps\n",
    ")"
   ]
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    "outputId": "2ed5a11b-c37d-4174-f622-ad7981635746"
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   "outputs": [
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train Epoch 1\n",
      "Loss: 0.09434445534061524 | Precision: 0.7523235682286378 | Recall: 0.7244104137370487 | F1: 0.7219966356411536\n"
     ]
    },
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    {
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     "text": [
      "\n",
      "Train Epoch 2\n",
      "Loss: 0.03390622173992597 | Precision: 0.8767971213013036 | Recall: 0.8582485647421727 | F1: 0.8591718740545049\n"
     ]
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    {
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     "text": [
      "\n",
      "Train Epoch 3\n",
      "Loss: 0.020380207003563557 | Precision: 0.9147022935944011 | Recall: 0.9039781701474965 | F1: 0.9043037439506161\n"
     ]
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     "text": [
      "\n",
      "Train Epoch 4\n",
      "Loss: 0.012612122387236014 | Precision: 0.9482234056243506 | Recall: 0.9409273121675431 | F1: 0.9416032074716452\n"
     ]
    },
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     "text": [
      "\n",
      "Train Epoch 5\n",
      "Loss: 0.009633440436755337 | Precision: 0.9618477842058498 | Recall: 0.9561781016813722 | F1: 0.9565209119693987\n"
     ]
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      "\n",
      "Train Epoch 6\n",
      "Loss: 0.006876613855388314 | Precision: 0.9726276241234588 | Recall: 0.9698155181437853 | F1: 0.9695152035981301\n"
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     "text": [
      "\n",
      "Train Epoch 7\n",
      "Loss: 0.005193366799630894 | Precision: 0.9775332222945661 | Recall: 0.975202721077997 | F1: 0.974734614483445\n"
     ]
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     "text": [
      "\n",
      "Train Epoch 8\n",
      "Loss: 0.0040791436070094274 | Precision: 0.9787714959462697 | Recall: 0.9771097573776222 | F1: 0.9763589480719121\n"
     ]
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     "text": [
      "\n",
      "Train Epoch 9\n",
      "Loss: 0.003198419363843641 | Precision: 0.9822195756297877 | Recall: 0.9810035889021076 | F1: 0.9802928331132665\n"
     ]
    },
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     "text": [
      "\n",
      "Train Epoch 10\n",
      "Loss: 0.0023902704910597093 | Precision: 0.9853252622675928 | Recall: 0.9840865986792314 | F1: 0.9838134294536605\n"
     ]
    }
   ],
   "source": [
    "# Train loop\n",
    "VALIDATE = False\n",
    "for i in range(epochs):\n",
    "    model.train()\n",
    "    train_loss = 0; train_pr = 0; train_rec = 0; train_f1 = 0\n",
    "    pbar = tqdm(total=len(train_dataloader))\n",
    "    for step, batch in enumerate(train_dataloader):\n",
    "        batch = tuple(t.to(device) for t in batch)\n",
    "        b_input_ids, b_input_mask, b_labels = batch\n",
    "        model.zero_grad()\n",
    "        logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)[0]\n",
    "        loss = ner_loss(logits, b_labels, b_input_mask, tag2idx[\"<sub>\"], model.config.num_labels)\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=max_grad_norm)\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "        precision, recall, f1 = calculate_metrics(logits.detach().cpu().numpy(), b_labels.detach().cpu().numpy(), b_input_ids.detach().cpu().numpy())\n",
    "        train_loss += loss.item(); train_pr += precision; train_rec += recall; train_f1 += f1\n",
    "        pbar.update(1)\n",
    "    pbar.close()\n",
    "    train_loss /= len(train_dataloader); train_pr /= len(train_dataloader)\n",
    "    train_rec /= len(train_dataloader); train_f1 /= len(train_dataloader) \n",
    "    print(\"Train Epoch {}\\nLoss: {} | Precision: {} | Recall: {} | F1: {}\".format(i+1, train_loss, train_pr, train_rec, train_f1))\n",
    "    if VALIDATE:\n",
    "        model.eval()\n",
    "        val_loss = 0; val_pr = 0; val_rec = 0; val_f1 = 0\n",
    "        predictions , true_labels = [], []\n",
    "        for batch in valid_dataloader:\n",
    "            batch = tuple(t.to(device) for t in batch)\n",
    "            b_input_ids, b_input_mask, b_labels = batch\n",
    "            with torch.no_grad():\n",
    "                logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)[0]\n",
    "                loss = ner_loss(logits, b_labels, b_input_mask, tag2idx[\"<sub>\"], model.config.num_labels)\n",
    "            precision, recall, f1 = calculate_metrics(logits.detach().cpu().numpy(), b_labels.detach().cpu().numpy(), b_input_ids.detach().cpu().numpy())\n",
    "            val_loss += loss.item(); val_pr += precision; val_rec += recall; val_f1 += f1\n",
    "        val_loss /= len(valid_dataloader); val_pr /= len(valid_dataloader)\n",
    "        val_rec /= len(valid_dataloader); val_f1 /= len(valid_dataloader) \n",
    "        print(\"Validation Epoch {}\\nLoss: {} | Precision: {} | Recall: {} | F1: {}\".format(i+1, val_loss, val_pr, val_rec, val_f1))"
   ]
  },
  {
   "cell_type": "code",
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    {
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       "version_major": 2,
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     "text": [
      "\n",
      "51533\n"
     ]
    }
   ],
   "source": [
    "# Test prediction\n",
    "predictions = []\n",
    "for test_sentence in tqdm(inputs_test):\n",
    "    tokenized_sentence = bert_tokenizer.encode(test_sentence, is_pretokenized=True)\n",
    "    input_ids = torch.tensor([tokenized_sentence]).cuda()\n",
    "    with torch.no_grad():\n",
    "        output = model(input_ids)\n",
    "    label_indices = np.argmax(output[0].cpu().numpy(), axis=2)[0]\n",
    "    idx_number = 1\n",
    "    for word in test_sentence:\n",
    "        if word == '[UNK]':\n",
    "            word_sub = '<unk>'\n",
    "        else:\n",
    "            word_sub = word\n",
    "        predictions.append([word_sub, tag_values[label_indices[idx_number]]])\n",
    "        idx_number += len(bert_tokenizer.tokenize(word))\n",
    "print(len(predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9nTQ9SdBTiDy"
   },
   "outputs": [],
   "source": [
    "# Generate submission file\n",
    "if (os.path.isfile('./predictions.zip')):\n",
    "    os.remove('./predictions.zip')\n",
    "\n",
    "if (not os.path.isdir('./predictions')):\n",
    "    os.mkdir('./predictions')\n",
    "else:\n",
    "    shutil.rmtree('./predictions')\n",
    "    os.mkdir('./predictions')\n",
    "\n",
    "f = open('predictions/predictions.txt', 'w')\n",
    "for word, tag in predictions:\n",
    "    f.write(word + ' ' + tag + '\\n')\n",
    "f.write('\\n')\n",
    "f.close()\n",
    "\n",
    "a = shutil.make_archive('predictions', 'zip', './predictions')"
   ]
  }
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