Token Classification/Monolingual/StrombergNLP-Twitter_pos_vcb/NER Project Using StrombergNLP Twitter_pos_vcb Dataset with PosEval.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monolingual Named Entity Recognition (Stromberg NLP Twitter P.o.S. Dataset)\n",
    "\n",
    "Dataset Source: https://huggingface.co/datasets/strombergnlp/twitter_pos_vcb"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Git LFS initialized.\n"
     ]
    }
   ],
   "source": [
    "import os, sys\n",
    "from collections import Counter, defaultdict\n",
    "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "import datasets\n",
    "from datasets import load_dataset, DatasetDict\n",
    "\n",
    "import torch\n",
    "from torch.nn.functional import cross_entropy\n",
    "\n",
    "import transformers\n",
    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
    "from transformers import DataCollatorForTokenClassification\n",
    "from transformers import Trainer, TrainingArguments\n",
    "\n",
    "import evaluate\n",
    "\n",
    "!git lfs install"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Library Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Python : 3.9.12\n",
      "           NumPy : 1.24.3\n",
      "          Pandas : 2.0.1\n",
      "           Torch : 2.0.0\n",
      "    Transformers : 4.28.1\n",
      "        datasets : 2.11.0\n",
      "         Sklearn : 1.2.2\n"
     ]
    }
   ],
   "source": [
    "print(\"Python :\".rjust(18), sys.version[0:6])\n",
    "print(\"NumPy :\".rjust(18), np.__version__)\n",
    "print(\"Pandas :\".rjust(18), pd.__version__)\n",
    "print(\"Torch :\".rjust(18), torch.__version__)\n",
    "print(\"Transformers :\".rjust(18), transformers.__version__)\n",
    "print(\"datasets :\".rjust(18), datasets.__version__)\n",
    "print(\"Sklearn :\".rjust(18), sklearn.__version__)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ingest English Subset of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset twitter_pos_vcb (/Users/briandunn/.cache/huggingface/datasets/strombergnlp___twitter_pos_vcb/twitter-pos-vcb/1.0.0/2c671a933c7ffb84cf124a1f987cd73fb883e6b3628ee4a01f5326f5aefc296a)\n"
     ]
    },
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     "data": {
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       "model_id": "53b552134dfb45baa2015acd2f34f925",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'tokens', 'pos_tags'],\n",
       "        num_rows: 159492\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_dataset(\"strombergnlp/twitter_pos_vcb\")\n",
    "\n",
    "data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split Dataset into DatasetDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape: (119619, 3)\n",
      "Testing data shape: (31899, 3)\n",
      "Validation data shape: (7974, 3)\n"
     ]
    }
   ],
   "source": [
    "# Split Dataset into train/test/eval datasets\n",
    "# then pack all of them into DatasetDict\n",
    "train_testvalid = data['train'].train_test_split(test_size=0.25)\n",
    "test_valid = train_testvalid['test'].train_test_split(test_size=0.80)\n",
    "\n",
    "ds = DatasetDict({\n",
    "    'train': train_testvalid['train'], \n",
    "    'test': test_valid['test'], \n",
    "    'eval': test_valid['train']})\n",
    "\n",
    "print('Training data shape:', ds['train'].shape)\n",
    "print('Testing data shape:', ds['test'].shape)\n",
    "print('Validation data shape:', ds['eval'].shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '144607', 'tokens': ['#food'], 'pos_tags': [48]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = ds['train'][12]\n",
    "\n",
    "example"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Feature Information About Each Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id: \n",
      "Value(dtype='string', id=None)\n",
      "\n",
      "tokens: \n",
      "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)\n",
      "\n",
      "pos_tags: \n",
      "Sequence(feature=ClassLabel(names=['\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB', 'RT', 'HT', 'USR', 'URL'], id=None), length=-1, id=None)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for k, v in ds[\"train\"].features.items():\n",
    "    print(f\"{k}: \\n{v}\\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Tag Values & Conversions Between String & Integer Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of tag values: \n",
      "['\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB', 'RT', 'HT', 'USR', 'URL']\n",
      "Number of NER Tags: \n",
      "51\n",
      "id2label: \n",
      "{0: '\"', 1: \"''\", 2: '#', 3: '$', 4: '(', 5: ')', 6: ',', 7: '.', 8: ':', 9: '``', 10: 'CC', 11: 'CD', 12: 'DT', 13: 'EX', 14: 'FW', 15: 'IN', 16: 'JJ', 17: 'JJR', 18: 'JJS', 19: 'LS', 20: 'MD', 21: 'NN', 22: 'NNP', 23: 'NNPS', 24: 'NNS', 25: 'NN|SYM', 26: 'PDT', 27: 'POS', 28: 'PRP', 29: 'PRP$', 30: 'RB', 31: 'RBR', 32: 'RBS', 33: 'RP', 34: 'SYM', 35: 'TO', 36: 'UH', 37: 'VB', 38: 'VBD', 39: 'VBG', 40: 'VBN', 41: 'VBP', 42: 'VBZ', 43: 'WDT', 44: 'WP', 45: 'WP$', 46: 'WRB', 47: 'RT', 48: 'HT', 49: 'USR', 50: 'URL'}\n",
      "label2id: \n",
      "{'\"': 0, \"''\": 1, '#': 2, '$': 3, '(': 4, ')': 5, ',': 6, '.': 7, ':': 8, '``': 9, 'CC': 10, 'CD': 11, 'DT': 12, 'EX': 13, 'FW': 14, 'IN': 15, 'JJ': 16, 'JJR': 17, 'JJS': 18, 'LS': 19, 'MD': 20, 'NN': 21, 'NNP': 22, 'NNPS': 23, 'NNS': 24, 'NN|SYM': 25, 'PDT': 26, 'POS': 27, 'PRP': 28, 'PRP$': 29, 'RB': 30, 'RBR': 31, 'RBS': 32, 'RP': 33, 'SYM': 34, 'TO': 35, 'UH': 36, 'VB': 37, 'VBD': 38, 'VBG': 39, 'VBN': 40, 'VBP': 41, 'VBZ': 42, 'WDT': 43, 'WP': 44, 'WP$': 45, 'WRB': 46, 'RT': 47, 'HT': 48, 'USR': 49, 'URL': 50}\n"
     ]
    }
   ],
   "source": [
    "pos_tag_values = ds['train'].features[f'pos_tags'].feature.names\n",
    "NUM_OF_LABELS = len(pos_tag_values)\n",
    "\n",
    "label2id = {tag: idx for idx, tag in enumerate(pos_tag_values)}\n",
    "id2label = {idx: tag for idx, tag in enumerate(pos_tag_values)}\n",
    "\n",
    "print(f\"List of tag values: \\n{pos_tag_values}\")\n",
    "print(f\"Number of NER Tags: \\n{NUM_OF_LABELS}\")\n",
    "print(f\"id2label: \\n{id2label}\")\n",
    "print(f\"label2id: \\n{label2id}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Values/Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_CKPT = \"bert-base-cased\"\n",
    "\n",
    "MODEL_NAME = f\"{MODEL_CKPT}-finetuned-Stromberg_NLP_Twitter-PoS_v2\"\n",
    "NUM_OF_EPOCHS = 2\n",
    "\n",
    "BATCH_SIZE = 16\n",
    "STRATEGY = \"epoch\"\n",
    "\n",
    "REPORTS_TO = \"tensorboard\"\n",
    "WEIGHT_DECAY = 0.01\n",
    "\n",
    "LR = 2e-5\n",
    "DEVICE = torch.device(\"cpu\")\n",
    "\n",
    "STEPS = 500"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to Tokenize & Align Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)\n",
    "\n",
    "def tokenize_and_align_labels(samples):\n",
    "    tokenized_inputs = tokenizer(samples[\"tokens\"], \n",
    "                                      truncation=True, \n",
    "                                      is_split_into_words=True)\n",
    "    \n",
    "    labels = []\n",
    "    \n",
    "    for idx, label in enumerate(samples[f\"pos_tags\"]):\n",
    "        word_ids = tokenized_inputs.word_ids(batch_index=idx)\n",
    "        prev_word_idx = None\n",
    "        label_ids = []\n",
    "        for word_idx in word_ids: # set special tokens to -100\n",
    "            if word_idx is None or word_idx == prev_word_idx:\n",
    "                label_ids.append(-100)\n",
    "            else:\n",
    "                label_ids.append(label[word_idx])\n",
    "            prev_word_idx = word_idx\n",
    "        labels.append(label_ids)\n",
    "    \n",
    "    tokenized_inputs[\"labels\"] = labels\n",
    "    return tokenized_inputs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply Above Function to Tokenize Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "version_minor": 0
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      "text/plain": [
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     "metadata": {},
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    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoded_ds = ds.map(tokenize_and_align_labels, \n",
    "                       batched=True, \n",
    "                       remove_columns=\n",
    "                        [\n",
    "                            'pos_tags', \n",
    "                            'tokens'\n",
    "                        ]\n",
    "                    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Data Collator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Custom Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.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 bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\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 = (AutoModelForTokenClassification.from_pretrained(\n",
    "    MODEL_CKPT,\n",
    "    num_labels=NUM_OF_LABELS,\n",
    "    id2label=id2label,\n",
    "    label2id=label2id\n",
    "    ).to(DEVICE))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Compute Metrics Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_list = pos_tag_values\n",
    "\n",
    "poseval = evaluate.load(\"poseval\")\n",
    "\n",
    "labels = [label_list[i] for i in example[f'pos_tags']]\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    predictions, labels = eval_preds\n",
    "    predictions = np.argmax(predictions, \n",
    "                            axis=2)\n",
    "    \n",
    "    true_predictions = [\n",
    "        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
    "        for prediction, label in zip(predictions, labels)\n",
    "    ]\n",
    "    \n",
    "    true_labels = [\n",
    "        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
    "        for prediction, label in zip(predictions, labels)\n",
    "    ]\n",
    "    \n",
    "    results = poseval.compute(predictions=true_predictions, \n",
    "                              references=true_labels)\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define TrainingArguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = TrainingArguments(\n",
    "    MODEL_NAME,\n",
    "    log_level=\"error\",\n",
    "    logging_first_step=True,\n",
    "    learning_rate=LR,\n",
    "    num_train_epochs=NUM_OF_EPOCHS,\n",
    "    per_device_train_batch_size=BATCH_SIZE,\n",
    "    per_device_eval_batch_size=BATCH_SIZE,\n",
    "    evaluation_strategy=STRATEGY,\n",
    "    report_to=REPORTS_TO,\n",
    "    disable_tqdm=False,\n",
    "    logging_steps=STEPS,\n",
    "    weight_decay=WEIGHT_DECAY,\n",
    "    save_strategy=STRATEGY,\n",
    "    hub_private_repo=True,\n",
    "    push_to_hub=True\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning https://huggingface.co/DunnBC22/bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2 into local empty directory.\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(model, \n",
    "                  args=args,\n",
    "                  data_collator=data_collator,\n",
    "                  compute_metrics=compute_metrics,\n",
    "                  tokenizer=tokenizer,\n",
    "                  train_dataset=encoded_ds[\"train\"],\n",
    "                  eval_dataset=encoded_ds[\"test\"]\n",
    "                  )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/briandunn/Documents/deep_learning/dl/lib/python3.9/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
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      "{'loss': 0.0805, 'learning_rate': 1.5318978199812758e-05, 'epoch': 0.47}\n",
      "{'loss': 0.0762, 'learning_rate': 1.4650260799786012e-05, 'epoch': 0.53}\n",
      "{'loss': 0.0761, 'learning_rate': 1.3981543399759264e-05, 'epoch': 0.6}\n",
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      "{'loss': 0.0676, 'learning_rate': 1.2644108599705764e-05, 'epoch': 0.74}\n",
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      "/Users/briandunn/Documents/deep_learning/dl/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/briandunn/Documents/deep_learning/dl/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/briandunn/Documents/deep_learning/dl/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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     "name": "stdout",
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      "/Users/briandunn/Documents/deep_learning/dl/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/briandunn/Documents/deep_learning/dl/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/briandunn/Documents/deep_learning/dl/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "{'train_runtime': 46884.0833, 'train_samples_per_second': 5.103, 'train_steps_per_second': 0.319, 'train_loss': 0.07854136392938528, 'epoch': 2.0}\n"
     ]
    }
   ],
   "source": [
    "train_results = trainer.train()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Push Model To Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Several commits (2) will be pushed upstream.\n",
      "The progress bars may be unreliable.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4083965e1c0d4157943310c63255013a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file pytorch_model.bin:   0%|          | 1.00/411M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1fce9c45929e46acb7bcc7e21cdd71ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file runs/Jul06_21-05-51_Brians-Mac-mini.local/events.out.tfevents.1688695556.Brians-Mac-mini.local.594…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2\n",
      "   71d28fc..53d736c  main -> main\n",
      "\n",
      "To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2\n",
      "   53d736c..1baf245  main -> main\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kwargs = {\n",
    "    \"tasks\": \"token-classification\",\n",
    "    \"tags\": ['token-classification'],\n",
    "}\n",
    "\n",
    "if args.push_to_hub:\n",
    "    trainer.push_to_hub('All DUNN!!!', **kwargs)\n",
    "else:\n",
    "    trainer.create_model_card(**kwargs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save & Log Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2\n",
      "   1baf245..4671fad  main -> main\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** train metrics *****\n",
      "  epoch                    =         2.0\n",
      "  train_loss               =      0.0785\n",
      "  train_runtime            = 13:01:24.08\n",
      "  train_samples_per_second =       5.103\n",
      "  train_steps_per_second   =       0.319\n"
     ]
    }
   ],
   "source": [
    "trainer.save_model()\n",
    "trainer.log_metrics(\"train\", train_results.metrics)\n",
    "trainer.save_metrics(\"train\", train_results.metrics)\n",
    "trainer.save_state()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Method to Apply to Validation Dataset (& Then Apply it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_pass_with_label(batch):\n",
    "    # Convert dict of lists to list of dicts suitable for data collator\n",
    "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
    "    # Pad inputs and labels and put all tensors on device\n",
    "    batch = data_collator(features)\n",
    "    input_ids = batch[\"input_ids\"].to(DEVICE)\n",
    "    attention_mask = batch[\"attention_mask\"].to(DEVICE)\n",
    "    labels = batch[\"labels\"].to(DEVICE)\n",
    "    with torch.no_grad():\n",
    "        # Pass data through model  \n",
    "        output = trainer.model(input_ids, \n",
    "                               attention_mask\n",
    "                               )\n",
    "        # Logit.size: [batch_size, sequence_length, classes]\n",
    "        predicted_label = torch.argmax(output.logits, \n",
    "                                       axis=-1\n",
    "                                       ).cpu().numpy()\n",
    "        \n",
    "    # Calculate loss per token after flattening batch dimension with view\n",
    "    loss = cross_entropy(output.logits.view(-1, 51), \n",
    "                         labels.view(-1), \n",
    "                         reduction=\"none\")\n",
    "    # Unflatten batch dimension and convert to numpy array\n",
    "    loss = loss.view(len(input_ids), -1).cpu().numpy()\n",
    "\n",
    "    return {\"loss\":loss, \"predicted_label\": predicted_label}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply Above Function to Entire Evaluation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter 'function'=<function forward_pass_with_label at 0x7fcce2d25280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n",
      "Loading cached processed dataset at /Users/briandunn/.cache/huggingface/datasets/strombergnlp___twitter_pos_vcb/twitter-pos-vcb/1.0.0/2c671a933c7ffb84cf124a1f987cd73fb883e6b3628ee4a01f5326f5aefc296a/cache-1c80317fa3b1799d.arrow\n"
     ]
    }
   ],
   "source": [
    "eval_set = encoded_ds['eval']\n",
    "\n",
    "eval_set = eval_set.remove_columns('id')\n",
    "\n",
    "eval_set = eval_set.map(forward_pass_with_label,\n",
    "                        batched=True,\n",
    "                        batch_size=32)\n",
    "\n",
    "eval_df = eval_set.to_pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean Up Padding Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input_ids</th>\n",
       "      <th>token_type_ids</th>\n",
       "      <th>attention_mask</th>\n",
       "      <th>labels</th>\n",
       "      <th>loss</th>\n",
       "      <th>predicted_label</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[101, 137, 1337, 2428, 2386, 1181, 2924, 7956,...</td>\n",
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       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
       "      <td>[IGN, USR, IGN, IGN, IGN, IGN, IGN, IGN, MD, V...</td>\n",
       "      <td>[0.0, 1.5497195e-06, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
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      ],
      "text/plain": [
       "                                           input_ids   \n",
       "0  [101, 137, 1337, 2428, 2386, 1181, 2924, 7956,...  \\\n",
       "\n",
       "                                     token_type_ids   \n",
       "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  \\\n",
       "\n",
       "                                     attention_mask   \n",
       "0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]  \\\n",
       "\n",
       "                                              labels   \n",
       "0  [IGN, USR, IGN, IGN, IGN, IGN, IGN, IGN, MD, V...  \\\n",
       "\n",
       "                                                loss   \n",
       "0  [0.0, 1.5497195e-06, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \\\n",
       "\n",
       "                                     predicted_label   \n",
       "0  [., USR, NNP, NNP, UH, UH, PRP, PRP, MD, VB, V...  \\\n",
       "\n",
       "                                        input_tokens  \n",
       "0  [[CLS], @, That, ##K, ##id, ##d, ##W, ##ill, s...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2label[-100] = \"IGN\"\n",
    "eval_df[\"input_tokens\"] = eval_df[\"input_ids\"].apply(\n",
    "    lambda x: tokenizer.convert_ids_to_tokens(x))\n",
    "eval_df[\"predicted_label\"] = eval_df[\"predicted_label\"].apply(\n",
    "    lambda x: [id2label[i] for i in x])\n",
    "eval_df[\"labels\"] = eval_df[\"labels\"].apply(\n",
    "    lambda x: [id2label[i] for i in x])\n",
    "eval_df['loss'] = eval_df.apply(\n",
    "    lambda x: x['loss'][:len(x['input_ids'])], axis=1)\n",
    "eval_df['predicted_label'] = eval_df.apply(\n",
    "    lambda x: x['predicted_label'][:len(x['input_ids'])], axis=1)\n",
    "eval_df.head(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unwrap Each Token Within Sample Separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>input_ids</th>\n",
       "      <th>token_type_ids</th>\n",
       "      <th>attention_mask</th>\n",
       "      <th>labels</th>\n",
       "      <th>loss</th>\n",
       "      <th>predicted_label</th>\n",
       "      <th>input_tokens</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>USR</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USR</td>\n",
       "      <td>@</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1431</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>MD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MD</td>\n",
       "      <td>should</td>\n",
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       "      <th>0</th>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PRP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PRP</td>\n",
       "      <td>me</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1171</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>RB</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RB</td>\n",
       "      <td>back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>.</td>\n",
       "      <td>:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>)</td>\n",
       "      <td>)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  input_ids token_type_ids attention_mask labels  loss predicted_label   \n",
       "0       137              0              1    USR   0.0             USR  \\\n",
       "0      1431              0              1     MD   0.0              MD   \n",
       "0       189              0              1     VB   0.0              VB   \n",
       "0      1143              0              1    PRP   0.0             PRP   \n",
       "0      1171              0              1     RB   0.0              RB   \n",
       "0       131              0              1      .   0.0               .   \n",
       "0       114              0              1      )   0.0               )   \n",
       "\n",
       "  input_tokens  \n",
       "0            @  \n",
       "0       should  \n",
       "0            t  \n",
       "0           me  \n",
       "0         back  \n",
       "0            :  \n",
       "0            )  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df_tokens = eval_df.apply(pd.Series.explode)\n",
    "eval_df_tokens = eval_df_tokens.query(\"labels != 'IGN'\")\n",
    "eval_df_tokens[\"loss\"] = eval_df_tokens[\"loss\"].astype(float).round(2)\n",
    "eval_df_tokens.head(7)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### See Which Tokens Have Accumulated Most Loss in Evaluation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>input_tokens</th>\n",
       "      <td>!</td>\n",
       "      <td>.</td>\n",
       "      <td>[UNK]</td>\n",
       "      <td>B</td>\n",
       "      <td>want</td>\n",
       "      <td>?</td>\n",
       "      <td>have</td>\n",
       "      <td>:</td>\n",
       "      <td>that</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1979</td>\n",
       "      <td>3500</td>\n",
       "      <td>107</td>\n",
       "      <td>96</td>\n",
       "      <td>181</td>\n",
       "      <td>822</td>\n",
       "      <td>358</td>\n",
       "      <td>2416</td>\n",
       "      <td>217</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.083</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>164.3</td>\n",
       "      <td>119.09</td>\n",
       "      <td>61.36</td>\n",
       "      <td>44.12</td>\n",
       "      <td>40.07</td>\n",
       "      <td>36.19</td>\n",
       "      <td>34.76</td>\n",
       "      <td>32.63</td>\n",
       "      <td>32.28</td>\n",
       "      <td>32.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0       1      2      3      4      5      6      7      8   \n",
       "input_tokens      !       .  [UNK]      B   want      ?   have      :   that  \\\n",
       "count          1979    3500    107     96    181    822    358   2416    217   \n",
       "mean          0.083   0.034  0.573   0.46  0.221  0.044  0.097  0.014  0.149   \n",
       "sum           164.3  119.09  61.36  44.12  40.07  36.19  34.76  32.63  32.28   \n",
       "\n",
       "                  9  \n",
       "input_tokens      G  \n",
       "count            43  \n",
       "mean          0.746  \n",
       "sum           32.08  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    eval_df_tokens.groupby(\"input_tokens\")[[\"loss\"]]\n",
    "    .agg([\"count\", \"mean\", \"sum\"])\n",
    "    .droplevel(level=0, axis=1)  # Get rid of multi-level columns\n",
    "    .sort_values(by=\"sum\", ascending=False)\n",
    "    .reset_index()\n",
    "    .round(3)\n",
    "    .head(10)\n",
    "    .T\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### See Which Label IDs Have Most Loss in Evaluation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <td>SYM</td>\n",
       "      <td>NNPS</td>\n",
       "      <td>WDT</td>\n",
       "      <td>VBN</td>\n",
       "      <td>''</td>\n",
       "      <td>RBR</td>\n",
       "      <td>JJR</td>\n",
       "      <td>``</td>\n",
       "      <td>NNP</td>\n",
       "      <td>)</td>\n",
       "      <td>VBD</td>\n",
       "      <td>VB</td>\n",
       "      <td>UH</td>\n",
       "      <td>VBP</td>\n",
       "      <td>EX</td>\n",
       "      <td>NNS</td>\n",
       "      <td>NN</td>\n",
       "      <td>JJ</td>\n",
       "      <td>VBZ</td>\n",
       "      <td>RP</td>\n",
       "      <td>VBG</td>\n",
       "      <td>JJS</td>\n",
       "      <td>RB</td>\n",
       "      <td>CD</td>\n",
       "      <td>MD</td>\n",
       "      <td>.</td>\n",
       "      <td>PRP$</td>\n",
       "      <td>IN</td>\n",
       "      <td>CC</td>\n",
       "      <td>PRP</td>\n",
       "      <td>:</td>\n",
       "      <td>RBS</td>\n",
       "      <td>DT</td>\n",
       "      <td>RT</td>\n",
       "      <td>TO</td>\n",
       "      <td>(</td>\n",
       "      <td>WRB</td>\n",
       "      <td>WP</td>\n",
       "      <td>,</td>\n",
       "      <td>USR</td>\n",
       "      <td>HT</td>\n",
       "      <td>URL</td>\n",
       "      <td>POS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>549</td>\n",
       "      <td>75</td>\n",
       "      <td>28</td>\n",
       "      <td>140</td>\n",
       "      <td>53</td>\n",
       "      <td>2194</td>\n",
       "      <td>109</td>\n",
       "      <td>1408</td>\n",
       "      <td>3729</td>\n",
       "      <td>2536</td>\n",
       "      <td>3963</td>\n",
       "      <td>16</td>\n",
       "      <td>2059</td>\n",
       "      <td>7797</td>\n",
       "      <td>3187</td>\n",
       "      <td>1489</td>\n",
       "      <td>331</td>\n",
       "      <td>1403</td>\n",
       "      <td>154</td>\n",
       "      <td>3963</td>\n",
       "      <td>644</td>\n",
       "      <td>1027</td>\n",
       "      <td>5314</td>\n",
       "      <td>1463</td>\n",
       "      <td>4630</td>\n",
       "      <td>1165</td>\n",
       "      <td>7337</td>\n",
       "      <td>3133</td>\n",
       "      <td>16</td>\n",
       "      <td>3848</td>\n",
       "      <td>2047</td>\n",
       "      <td>1470</td>\n",
       "      <td>49</td>\n",
       "      <td>624</td>\n",
       "      <td>383</td>\n",
       "      <td>1076</td>\n",
       "      <td>5175</td>\n",
       "      <td>2011</td>\n",
       "      <td>914</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.53</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.075</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>13.06</td>\n",
       "      <td>22.14</td>\n",
       "      <td>12.3</td>\n",
       "      <td>158.29</td>\n",
       "      <td>21.29</td>\n",
       "      <td>7.53</td>\n",
       "      <td>29.19</td>\n",
       "      <td>10.81</td>\n",
       "      <td>437.27</td>\n",
       "      <td>15.14</td>\n",
       "      <td>184.23</td>\n",
       "      <td>454.46</td>\n",
       "      <td>308.95</td>\n",
       "      <td>453.92</td>\n",
       "      <td>1.78</td>\n",
       "      <td>208.72</td>\n",
       "      <td>555.86</td>\n",
       "      <td>218.38</td>\n",
       "      <td>94.68</td>\n",
       "      <td>20.65</td>\n",
       "      <td>83.53</td>\n",
       "      <td>7.95</td>\n",
       "      <td>128.87</td>\n",
       "      <td>20.33</td>\n",
       "      <td>31.37</td>\n",
       "      <td>157.4</td>\n",
       "      <td>36.17</td>\n",
       "      <td>59.48</td>\n",
       "      <td>12.17</td>\n",
       "      <td>69.21</td>\n",
       "      <td>25.74</td>\n",
       "      <td>0.13</td>\n",
       "      <td>26.11</td>\n",
       "      <td>11.19</td>\n",
       "      <td>7.32</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1      2       3      4      5      6      7       8    \n",
       "labels    SYM   NNPS    WDT     VBN     ''    RBR    JJR     ``     NNP  \\\n",
       "count       2      6      4     549     75     28    140     53    2194   \n",
       "mean     6.53   3.69  3.075   0.288  0.284  0.269  0.209  0.204   0.199   \n",
       "sum     13.06  22.14   12.3  158.29  21.29   7.53  29.19  10.81  437.27   \n",
       "\n",
       "           9       10      11      12      13     14      15      16      17   \n",
       "labels      )     VBD      VB      UH     VBP     EX     NNS      NN      JJ  \\\n",
       "count     109    1408    3729    2536    3963     16    2059    7797    3187   \n",
       "mean    0.139   0.131   0.122   0.122   0.115  0.111   0.101   0.071   0.069   \n",
       "sum     15.14  184.23  454.46  308.95  453.92   1.78  208.72  555.86  218.38   \n",
       "\n",
       "           18     19     20     21      22     23     24     25     26     27   \n",
       "labels    VBZ     RP    VBG    JJS      RB     CD     MD      .   PRP$     IN  \\\n",
       "count    1489    331   1403    154    3963    644   1027   5314   1463   4630   \n",
       "mean    0.064  0.062   0.06  0.052   0.033  0.032  0.031   0.03  0.025  0.013   \n",
       "sum     94.68  20.65  83.53   7.95  128.87  20.33  31.37  157.4  36.17  59.48   \n",
       "\n",
       "           28     29     30     31     32     33     34     35     36     37   \n",
       "labels     CC    PRP      :    RBS     DT     RT     TO      (    WRB     WP  \\\n",
       "count    1165   7337   3133     16   3848   2047   1470     49    624    383   \n",
       "mean     0.01  0.009  0.008  0.008  0.007  0.005  0.005  0.002  0.002  0.002   \n",
       "sum     12.17  69.21  25.74   0.13  26.11  11.19   7.32   0.12   1.48   0.71   \n",
       "\n",
       "          38    39    40   41   42  \n",
       "labels     ,   USR    HT  URL  POS  \n",
       "count   1076  5175  2011  914  108  \n",
       "mean     0.0   0.0   0.0  0.0  0.0  \n",
       "sum     0.23  0.34  0.04  0.0  0.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    eval_df_tokens.groupby(\"labels\")[[\"loss\"]] \n",
    "    .agg([\"count\", \"mean\", \"sum\"])\n",
    "    .droplevel(level=0, axis=1)\n",
    "    .sort_values(by=\"mean\", ascending=False)\n",
    "    .reset_index()\n",
    "    .round(3)\n",
    "    .fillna(0)\n",
    "    .T\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Function to Display Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "def plot_confusion_matrix(y_preds, y_true, labels):\n",
    "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
    "    fig, ax = plt.subplots(figsize=(30, 30))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
    "    plt.title(\"Normalized Confusion Matrix\")\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_token_list = list(set(eval_df_tokens['labels']))\n",
    "\n",
    "plot_confusion_matrix(eval_df_tokens[\"labels\"], eval_df_tokens[\"predicted_label\"],\n",
    "                      eval_token_list)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define & Call Function to Display Example Token Sequences Along With Labels & Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tokens</th>\n",
       "      <td>Go</td>\n",
       "      <td>cardinal</td>\n",
       "      <td>##s</td>\n",
       "      <td>!</td>\n",
       "      <td>!</td>\n",
       "      <td>!</td>\n",
       "      <td>!</td>\n",
       "      <td>#</td>\n",
       "      <td>game</td>\n",
       "      <td>##2</td>\n",
       "      <td>#</td>\n",
       "      <td>NL</td>\n",
       "      <td>##CS</td>\n",
       "      <td>[SEP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <td>VB</td>\n",
       "      <td>NNPS</td>\n",
       "      <td>IGN</td>\n",
       "      <td>UH</td>\n",
       "      <td>.</td>\n",
       "      <td>UH</td>\n",
       "      <td>.</td>\n",
       "      <td>HT</td>\n",
       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
       "      <td>HT</td>\n",
       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preds</th>\n",
       "      <td>VB</td>\n",
       "      <td>NNS</td>\n",
       "      <td>NNS</td>\n",
       "      <td>.</td>\n",
       "      <td>UH</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>HT</td>\n",
       "      <td>HT</td>\n",
       "      <td>HT</td>\n",
       "      <td>HT</td>\n",
       "      <td>NNP</td>\n",
       "      <td>HT</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>losses</th>\n",
       "      <td>0.03</td>\n",
       "      <td>9.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.61</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1     2     3     4     5     6     7     8     9     10   \n",
       "tokens    Go  cardinal   ##s     !     !     !     !     #  game   ##2     #  \\\n",
       "labels    VB      NNPS   IGN    UH     .    UH     .    HT   IGN   IGN    HT   \n",
       "preds     VB       NNS   NNS     .    UH     .     .    HT    HT    HT    HT   \n",
       "losses  0.03      9.29  0.00  8.61  4.15  5.25  0.00  0.00  0.00  0.00  0.00   \n",
       "\n",
       "          11    12     13  \n",
       "tokens    NL  ##CS  [SEP]  \n",
       "labels   IGN   IGN    IGN  \n",
       "preds    NNP    HT      .  \n",
       "losses  0.00  0.00   0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>38</th>\n",
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       "    <tr>\n",
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       "      <td>HT</td>\n",
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       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
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       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
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       "      <td>IGN</td>\n",
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       "      <td>HT</td>\n",
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       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
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       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
       "      <td>IGN</td>\n",
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       "      <td>IGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preds</th>\n",
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       "      <td>NN</td>\n",
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       "      <td>NN</td>\n",
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       "      <td>NNP</td>\n",
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       "      <td>NN</td>\n",
       "      <td>NN</td>\n",
       "      <td>NN</td>\n",
       "      <td>NN</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>losses</th>\n",
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       "          0     1     2     3     4     5     6     7     8     9     10   \n",
       "tokens     #     W  ##EL   ##O  ##VE  ##IF   ##Y  ##MU  ##SI   ##C  ##VI  \\\n",
       "labels    HT   IGN   IGN   IGN   IGN   IGN   IGN   IGN   IGN   IGN   IGN   \n",
       "preds     HT   NNP   NNP   NNP   NNP    NN    NN    NN    NN    NN    NN   \n",
       "losses  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00   \n",
       "\n",
       "          11    12    13    14    15    16    17    18    19    20    21   \n",
       "tokens  ##DE   ##O     !     !     .     .     .     #     W  ##EL   ##O  \\\n",
       "labels   IGN   IGN     .    UH     .    UH     .    HT   IGN   IGN   IGN   \n",
       "preds     NN    NN     .     .     :     :     .    HT   NNP   NNP   NNP   \n",
       "losses  0.00  0.00  0.00  2.75  5.80  4.03  0.00  0.00  0.00  0.00  0.00   \n",
       "\n",
       "          22    23    24    25    26    27    28    29    30    31    32   \n",
       "tokens  ##VE  ##IF   ##Y  ##MU  ##SI   ##C  ##VI  ##DE   ##O     !     !  \\\n",
       "labels   IGN   IGN   IGN   IGN   IGN   IGN   IGN   IGN   IGN     .    UH   \n",
       "preds    NNP    NN    NN    NN    NN    NN    NN    NN    NN     .     .   \n",
       "losses  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  2.96   \n",
       "\n",
       "          33    34    35    36    37    38    39    40    41    42    43   \n",
       "tokens     .     .     .     #     W  ##EL   ##O  ##VE  ##IF   ##Y  ##MU  \\\n",
       "labels     .    UH     .    HT   IGN   IGN   IGN   IGN   IGN   IGN   IGN   \n",
       "preds      :     :     .    HT   NNP   NNP   NNP   NNP    NN    NN    NN   \n",
       "losses  5.23  3.79  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00   \n",
       "\n",
       "          44    45    46    47    48    49    50     51  \n",
       "tokens  ##SI   ##C  ##VI  ##DE   ##O     !     !  [SEP]  \n",
       "labels   IGN   IGN   IGN   IGN   IGN     .     .    IGN  \n",
       "preds     NN    NN    NN    NN    NN     .     .      .  \n",
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       "          0         1     2     3     4     5     6     7     8     9     10   \n",
       "tokens   The  comments     !     !     !     !     !     !     O   ##m   ##f  \\\n",
       "labels    DT       NNS     .     .    UH     .    UH     .    UH   IGN   IGN   \n",
       "preds     DT       NNS     .    UH     .    UH     .     .    UH    UH    UH   \n",
       "losses  0.00      0.00  0.00  5.94  5.39  2.34  3.79  0.00  0.00  0.00  0.00   \n",
       "\n",
       "          11    12    13    14    15    16    17    18    19     20    21   \n",
       "tokens   ##g     .     I    ca     n     '     t  with  this  video     .  \\\n",
       "labels   IGN     .   PRP    MD    RB   IGN   IGN    IN    DT     NN     .   \n",
       "preds     UH     .   PRP    MD    RB    RB    RB    IN    DT     NN     .   \n",
       "losses  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00   0.00  0.00   \n",
       "\n",
       "          22    23    24    25     26    27     28    29    30    31    32   \n",
       "tokens     I  feel  like    my  heart    is  going    to  jump   and   run  \\\n",
       "labels   PRP   VBP    IN  PRP$     NN   VBZ    VBG    TO    VB    CC    VB   \n",
       "preds    PRP   VBP    IN  PRP$     NN   VBZ    VBG    TO    VB    CC    VB   \n",
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       "\n",
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   ],
   "source": [
    "def get_samples(df):\n",
    "    for _, row in df.iterrows():\n",
    "        labels, preds, tokens, losses = [], [], [], []\n",
    "        for i, mask in enumerate(row[\"attention_mask\"]):\n",
    "            if i not in {0, len(row[\"attention_mask\"])}:\n",
    "                labels.append(row[\"labels\"][i])\n",
    "                preds.append(row[\"predicted_label\"][i])\n",
    "                tokens.append(row[\"input_tokens\"][i])\n",
    "                losses.append(f\"{row['loss'][i]:.2f}\")\n",
    "        eval_df_tmp = pd.DataFrame({\"tokens\": tokens, \"labels\": labels, \n",
    "                               \"preds\": preds, \"losses\": losses}).T\n",
    "        yield eval_df_tmp\n",
    "\n",
    "eval_df[\"total_loss\"] = eval_df[\"loss\"].apply(sum)\n",
    "eval_df_tmp = eval_df.sort_values(by=\"total_loss\", ascending=False).head(3)\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "for sample in get_samples(eval_df_tmp):\n",
    "    display(sample)"
   ]
  },
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   "source": [
    "### Notes & Other Takeaways From This Project\n",
    "\n",
    "****\n",
    "- Normally, I like to include some nice visualizations, which I (unfortunately) did not do with this project. Because I wanted to focus on the main part of the project (for this project and many others), I forewent the visualizations. I intend on completed some as an acillary project after completing the currently list of projects that I have.\n",
    "\n",
    "- This model struggled to identify (any) 'JJR' and 'WRB' tokens in the error analysis. For 'JJR' tokens, this makes sense because there were only 140 'JJR' samples. On the other hand there were 624 samples of 'WRB' tokens.\n",
    "\n",
    "- The model also struggled to accurately identify 'RBR' and 'MD' tokens in the error analysis. It makes sense that this model performed poorly handling 'RBR' tokens as there were only 28 samples, but the same cannot be said about 'MD' tokens as there were 1027 'MD' tokens.\n",
    "\n",
    "- The original metrics from the model stuggled to accurately identify '$', 'nnps', 'sym', and 'fw' tokens. This just cements my next comment even more. \n",
    "\n",
    "- I was hesitant to subclass the trainer to handle class imbalance since there were 51 classes; however, given the results, I have rethought that and I will fine-tune a version of this model with a subclassed trainer.\n",
    "\n",
    "****"
   ]
  },
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   "source": [
    "### Citations\n",
    "\n",
    "- Model Checkpoint\n",
    "\n",
    "    > @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}}\n",
    "\n",
    "- Dataset\n",
    "\n",
    "    > @inproceedings{derczynski2013twitter, title={Twitter part-of-speech tagging for all: Overcoming sparse and noisy data}, author={Derczynski, Leon and Ritter, Alan and Clark, Sam and Bontcheva, Kalina}, booktitle={Proceedings of the international conference recent advances in natural language ranlp 2013}, pages={198--206}, year={2013}}\n",
    "\n",
    "- Metric (PosEval)\n",
    "\n",
    "    > @article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}}"
   ]
  }
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