labs/notebooks_2024/lab_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ILsAojF_nXzT"
   },
   "source": [
    "# Link to the lab\n",
    "\n",
    "https://tinyurl.com/inlplab5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KVkKP3mNWP4c"
   },
   "source": [
    "# Setup\n",
    "\n",
    "We'll use fasttext wiki embeddings in our embedding layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "shI-n-rp8nU2",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "077dc499-179c-488b-fa2d-d79c881c5d56",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting sklearn\n",
      "  Using cached sklearn-0.0-py2.py3-none-any.whl\n",
      "Collecting scikit-learn\n",
      "  Using cached scikit_learn-1.1.2-cp310-cp310-macosx_10_9_x86_64.whl (8.7 MB)\n",
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      "Installing collected packages: threadpoolctl, scikit-learn, sklearn\n",
      "Successfully installed scikit-learn-1.1.2 sklearn-0.0 threadpoolctl-3.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install fasttext\n",
    "!pip install datasets\n",
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qTxj2GUD86Mt"
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "K23XIfU19JC6"
   },
   "outputs": [],
   "source": [
    "import io\n",
    "from math import log\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "import torch\n",
    "import random\n",
    "from math import log\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "import numpy as np\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch import nn\n",
    "from torch.optim import Adam\n",
    "from torch.optim.lr_scheduler import ExponentialLR, CyclicLR\n",
    "from typing import List, Tuple, AnyStr\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy\n",
    "from datasets import load_dataset\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import torch.nn.functional as F\n",
    "import heapq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "1WG_TMG29Jkh"
   },
   "outputs": [],
   "source": [
    "def enforce_reproducibility(seed=42):\n",
    "    # Sets seed manually for both CPU and CUDA\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    # For atomic operations there is currently \n",
    "    # no simple way to enforce determinism, as\n",
    "    # the order of parallel operations is not known.\n",
    "    # CUDNN\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    # System based\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "enforce_reproducibility()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y0-F6_Wb9Ams"
   },
   "source": [
    "# Sequence Classification - recap\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kk7Nm4aD1Le_"
   },
   "source": [
    "\n",
    "Sequence classification is the task of \n",
    "- predicting a class (e.g., POS tag) for each separate token in a textual input\n",
    "- label tokens as beginning (B), inside (I), or outside (O) \n",
    "- predicting which tokens from the input belong to a span, e.g.:\n",
    "  - which tokens from a document answer a given question (extractive QA)\n",
    "![](https://rajpurkar.github.io/mlx/qa-and-squad/example-squad.png)\n",
    "  - which tokens in a news article contain propagandistic techniques\n",
    "![](https://d3i71xaburhd42.cloudfront.net/237a2b25e1ced676b0ebe8ccaa0cd4b7c5adac6b/5-Figure2-1.png)\n",
    "  - the spans can be of different types, e.g. type of a Named Entity (NE) -- Person, Location, Organisation\n",
    "  - ([More datasets for structured prediction](https://huggingface.co/datasets?languages=languages:en&task_categories=task_categories:structure-prediction&sort=downloads))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OHp8Z6Pc89h7"
   },
   "source": [
    "## Named entity recognition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "viwPhyqMaQhi"
   },
   "source": [
    "\n",
    "\n",
    "- identify the **entities** that appear in a document and their types\n",
    "- e.g., extract from the following sentence all names of the people, locations, and organizations:\n",
    "\n",
    "<style type=\"text/css\">\n",
    ".tg  {border-collapse:collapse;border-spacing:0;}\n",
    ".tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
    "  overflow:hidden;padding:10px 5px;word-break:normal;}\n",
    ".tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
    "  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\n",
    ".tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top}\n",
    "</style>\n",
    "<table class=\"tg\">\n",
    "<tbody>\n",
    " <tr>\n",
    "    <th class=\"tg-0pky\">Sundar</th>\n",
    "    <th class=\"tg-0pky\">Pichai</th>\n",
    "    <th class=\"tg-0pky\">is</th>\n",
    "    <th class=\"tg-0pky\">the</th>\n",
    "    <th class=\"tg-0pky\">CEO</th>\n",
    "    <th class=\"tg-0pky\">of</th>\n",
    "    <th class=\"tg-0pky\">Alphabet</th>\n",
    "    <th class=\"tg-0pky\">,</th>\n",
    "    <th class=\"tg-0pky\">located</th>\n",
    "    <th class=\"tg-0pky\">in</th>\n",
    "    <th class=\"tg-0pky\">Mountain</th>\n",
    "    <th class=\"tg-0pky\">View</th>\n",
    "    <th class=\"tg-0pky\">,</th>\n",
    "    <th class=\"tg-0pky\">CA</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">PER</td>\n",
    "    <td class=\"tg-0pky\">PER</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">ORG</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">LOC</td>\n",
    "    <td class=\"tg-0pky\">LOC</td>\n",
    "    <td class=\"tg-0pky\">LOC</td>\n",
    "    <td class=\"tg-0pky\">LOC</td>\n",
    "  </tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "- we have labelled all of the tokens associate with their classes as the given type (PER: Person, ORG: Organization, LOC: Location, O: Outside). **Question: What are some issues that could arise as a result of this tagging?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bG7fTfhRdulS"
   },
   "source": [
    "In practice, we will also want to denote which tokens are the beginning of an entity, and which tokens are inside the full entity span, giving the following tagging:\n",
    "\n",
    "<style type=\"text/css\">\n",
    ".tg  {border-collapse:collapse;border-spacing:0;}\n",
    ".tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
    "  overflow:hidden;padding:10px 5px;word-break:normal;}\n",
    ".tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
    "  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\n",
    ".tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top}\n",
    "</style>\n",
    "<table class=\"tg\">\n",
    "<tbody>\n",
    " <tr>\n",
    "    <th class=\"tg-0pky\">Sundar</th>\n",
    "    <th class=\"tg-0pky\">Pichai</th>\n",
    "    <th class=\"tg-0pky\">is</th>\n",
    "    <th class=\"tg-0pky\">the</th>\n",
    "    <th class=\"tg-0pky\">CEO</th>\n",
    "    <th class=\"tg-0pky\">of</th>\n",
    "    <th class=\"tg-0pky\">Alphabet</th>\n",
    "    <th class=\"tg-0pky\">,</th>\n",
    "    <th class=\"tg-0pky\">located</th>\n",
    "    <th class=\"tg-0pky\">in</th>\n",
    "    <th class=\"tg-0pky\">Mountain</th>\n",
    "    <th class=\"tg-0pky\">View</th>\n",
    "    <th class=\"tg-0pky\">,</th>\n",
    "    <th class=\"tg-0pky\">CA</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">B-PER</td>\n",
    "    <td class=\"tg-0pky\">I-PER</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">B-ORG</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">O</td>\n",
    "    <td class=\"tg-0pky\">B-LOC</td>\n",
    "    <td class=\"tg-0pky\">I-LOC</td>\n",
    "    <td class=\"tg-0pky\">I-LOC</td>\n",
    "    <td class=\"tg-0pky\">I-LOC</td>\n",
    "  </tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "**Question: What are some other tagging schemes that you think could be good?**\n",
    "\n",
    "Modeling the dependencies between the predictions can be useful: for example knowing that the previous tag was `B-PER` influences whether or not the current tag will be `I-PER` or `O` or `I-LOC`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uOi3HWggedf9"
   },
   "source": [
    "## Download and prepare the data\n",
    "\n",
    "We'll use a small set of Wikipedia data labelled with people, locations, organizations, and \"miscellaneous\" entities."
   ]
  },
  {
   "cell_type": "code",
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    },
    "id": "BoEPDmb6QTw5",
    "outputId": "b4e2e71c-63f8-46f2-ae37-f4a3957850a1"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/2.58k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "1267c3d89f2746778cad1cbc7d5548b8",
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset conll2003/conll2003 (download: 959.94 KiB, generated: 9.78 MiB, post-processed: Unknown size, total: 10.72 MiB) to /Users/knf792/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bbb7b119ba842e384b238c600a20fa8",
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      ]
     },
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      ]
     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Dataset conll2003 downloaded and prepared to /Users/knf792/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79ea6d948af040d6b4245ecc090af3f3",
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    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
       "        num_rows: 14041\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
       "        num_rows: 3250\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
       "        num_rows: 3453\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = load_dataset(\"conll2003\")\n",
    "datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qU6qEpOTQknb",
    "outputId": "294acb1c-fab4-40e4-fbc9-5e6067f9e2e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset({\n",
      "    features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
      "    num_rows: 14041\n",
      "})\n",
      "{'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}\n",
      "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None)\n",
      "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']\n"
     ]
    }
   ],
   "source": [
    "print(datasets['train'])\n",
    "print(datasets['train'][0])\n",
    "print(datasets[\"train\"].features[f\"ner_tags\"])\n",
    "print(datasets[\"train\"].features[f\"ner_tags\"].feature.names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pIZdSxg6fOHb"
   },
   "source": [
    "We'll create the word embedding space:\n",
    "- with FastText pretrained embeddings\n",
    "- using all of the *vocabulary from the train and dev splits*, plus the most frequent tokens from the trained word embeddings. This will reduce the embeddings size!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ABBIslsS9_1U",
    "outputId": "d393b804-b108-4336-f916-b48bbc54e14b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-10-06 09:18:40--  https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip\n",
      "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 172.67.9.4, 104.22.74.142, 104.22.75.142\n",
      "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|172.67.9.4|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 681808098 (650M) [application/zip]\n",
      "Saving to: ‘wiki-news-300d-1M.vec.zip’\n",
      "\n",
      "wiki-news-300d-1M.v 100%[===================>] 650,22M  11,9MB/s    in 49s     \n",
      "\n",
      "2022-10-06 09:19:30 (13,3 MB/s) - ‘wiki-news-300d-1M.vec.zip’ saved [681808098/681808098]\n",
      "\n",
      "Archive:  wiki-news-300d-1M.vec.zip\n",
      "  inflating: wiki-news-300d-1M.vec   \n"
     ]
    }
   ],
   "source": [
    "!wget https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip\n",
    "!unzip wiki-news-300d-1M.vec.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "AU1ldp1VArxU"
   },
   "outputs": [],
   "source": [
    "# Reduce down to our vocabulary and word embeddings\n",
    "def load_vectors(fname, vocabulary):\n",
    "    fin = io.open(fname, 'r', encoding='utf-8', newline='\\n', errors='ignore')\n",
    "    n, d = map(int, fin.readline().split())\n",
    "    tag_names = datasets[\"train\"].features[f\"ner_tags\"].feature.names\n",
    "    final_vocab = tag_names + ['[PAD]', '[UNK]', '[BOS]', '[EOS]']\n",
    "    final_vectors = [np.random.normal(size=(300,)) for _ in range(len(final_vocab))]\n",
    "    for j,line in enumerate(fin):\n",
    "        tokens = line.rstrip().split(' ')\n",
    "        if tokens[0] in vocabulary or len(final_vocab) < 30000:\n",
    "            final_vocab.append(tokens[0])\n",
    "            final_vectors.append(np.array(list(map(float, tokens[1:]))))\n",
    "    return final_vocab, np.vstack(final_vectors)\n",
    "\n",
    "class FasttextTokenizer:\n",
    "    def __init__(self, vocabulary):\n",
    "        self.vocab = {}\n",
    "        for j,l in enumerate(vocabulary):\n",
    "            self.vocab[l.strip()] = j\n",
    "\n",
    "    def encode(self, text):\n",
    "        # Text is assumed to be tokenized\n",
    "        return [self.vocab[t] if t in self.vocab else self.vocab['[UNK]'] for t in text]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4OgHYnqV-CzF",
    "outputId": "cead7528-df83-4a52-8261-ba9f26ff78c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of vocabulary:  40630\n"
     ]
    }
   ],
   "source": [
    "vocabulary = (set([t for s in datasets['train'] for t in s['tokens']]) | set([t for s in datasets['validation'] for t in s['tokens']]))\n",
    "vocabulary, pretrained_embeddings = load_vectors('wiki-news-300d-1M.vec', vocabulary)\n",
    "print('size of vocabulary: ', len(vocabulary))\n",
    "tokenizer = FasttextTokenizer(vocabulary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "03GlNGdjffvJ"
   },
   "source": [
    "The main difference in the dataset reading and collation functions is that we now return a sequence of labels instead of a single label as in text classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "DDNdg8kNCYxa"
   },
   "outputs": [],
   "source": [
    "def collate_batch_bilstm(input_data: Tuple) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n",
    "    input_ids = [tokenizer.encode(i['tokens']) for i in input_data]\n",
    "    seq_lens = [len(i) for i in input_ids]\n",
    "    labels = [i['ner_tags'] for i in input_data]\n",
    "\n",
    "    max_length = max([len(i) for i in input_ids])\n",
    "\n",
    "    input_ids = [(i + [0] * (max_length - len(i))) for i in input_ids]\n",
    "    labels = [(i + [0] * (max_length - len(i))) for i in labels] # 0 is the id of the O tag\n",
    "\n",
    "    assert (all(len(i) == max_length for i in input_ids))\n",
    "    assert (all(len(i) == max_length for i in labels))\n",
    "    return torch.tensor(input_ids), torch.tensor(seq_lens), torch.tensor(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EAJsgXF_IZUQ",
    "outputId": "b25fbeae-e7f8-4794-89a0-72b2f9bb8063"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[36231,    48,    10, 33561, 30770,  8120, 31121, 21803,    10, 36750,\n",
       "             15]]),\n",
       " tensor([11]),\n",
       " tensor([[0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0]]))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_dl = DataLoader(datasets['validation'], batch_size=1, shuffle=False, collate_fn=collate_batch_bilstm, num_workers=0)\n",
    "next(iter(dev_dl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AIHamPlSIgS5",
    "outputId": "549bf33b-8cc7-4720-c255-5a877992f3d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': '0', 'tokens': ['CRICKET', '-', 'LEICESTERSHIRE', 'TAKE', 'OVER', 'AT', 'TOP', 'AFTER', 'INNINGS', 'VICTORY', '.'], 'pos_tags': [22, 8, 22, 22, 15, 22, 22, 22, 22, 21, 7], 'chunk_tags': [11, 0, 11, 12, 13, 11, 12, 12, 12, 12, 0], 'ner_tags': [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0]}\n",
      "(tensor([[36231,    48,    10, 33561, 30770,  8120, 31121, 21803,    10, 36750,\n",
      "            15]]), tensor([11]), tensor([[0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0]]))\n"
     ]
    }
   ],
   "source": [
    "print(datasets['validation'][0])\n",
    "print(collate_batch_bilstm([datasets['validation'][0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oo6sp4It9Txz"
   },
   "source": [
    "# Creating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cQIYJ0Q_gILv"
   },
   "source": [
    "## LSTM model for sequence classification\n",
    "\n",
    "You'll notice that the BiLSTM model is mostly the same from the text classification and language modeling labs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "nVsVJgToVrdz"
   },
   "outputs": [],
   "source": [
    "# Define the model\n",
    "class BiLSTM(nn.Module):\n",
    "    \"\"\"\n",
    "    Basic BiLSTM-CRF network\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "            self,\n",
    "            pretrained_embeddings: torch.tensor,\n",
    "            lstm_dim: int,\n",
    "            dropout_prob: float = 0.1,\n",
    "            n_classes: int = 2\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Initializer for basic BiLSTM network\n",
    "        :param pretrained_embeddings: A tensor containing the pretrained BPE embeddings\n",
    "        :param lstm_dim: The dimensionality of the BiLSTM network\n",
    "        :param dropout_prob: Dropout probability\n",
    "        :param n_classes: The number of output classes\n",
    "        \"\"\"\n",
    "\n",
    "        # First thing is to call the superclass initializer\n",
    "        super(BiLSTM, self).__init__()\n",
    "\n",
    "        # We'll define the network in a ModuleDict, which makes organizing the model a bit nicer\n",
    "        # The components are an embedding layer, a 2 layer BiLSTM, and a feed-forward output layer\n",
    "        self.model = nn.ModuleDict({\n",
    "            'embeddings': nn.Embedding.from_pretrained(pretrained_embeddings, padding_idx=pretrained_embeddings.shape[0] - 1),\n",
    "            'bilstm': nn.LSTM(\n",
    "                pretrained_embeddings.shape[1],  # input size\n",
    "                lstm_dim,  # hidden size\n",
    "                2,  # number of layers\n",
    "                batch_first=True,\n",
    "                dropout=dropout_prob,\n",
    "                bidirectional=True),\n",
    "            'ff': nn.Linear(2*lstm_dim, n_classes),\n",
    "        })\n",
    "        self.n_classes = n_classes\n",
    "        self.loss = nn.CrossEntropyLoss()\n",
    "        # Initialize the weights of the model\n",
    "        self._init_weights()\n",
    "\n",
    "    def _init_weights(self):\n",
    "        all_params = list(self.model['bilstm'].named_parameters()) + \\\n",
    "                     list(self.model['ff'].named_parameters())\n",
    "        for n,p in all_params:\n",
    "            if 'weight' in n:\n",
    "                nn.init.xavier_normal_(p)\n",
    "            elif 'bias' in n:\n",
    "                nn.init.zeros_(p)\n",
    "\n",
    "    def forward(self, inputs, input_lens, hidden_states = None, labels = None):\n",
    "        \"\"\"\n",
    "        Defines how tensors flow through the model\n",
    "        :param inputs: (b x sl) The IDs into the vocabulary of the input samples\n",
    "        :param input_lens: (b) The length of each input sequence\n",
    "        :param labels: (b) The label of each sample\n",
    "        :return: (loss, logits) if `labels` is not None, otherwise just (logits,)\n",
    "        \"\"\"\n",
    "\n",
    "        # Get embeddings (b x sl x edim)\n",
    "        embeds = self.model['embeddings'](inputs)\n",
    "\n",
    "        # Pack padded: This is necessary for padded batches input to an RNN - https://stackoverflow.com/questions/51030782/why-do-we-pack-the-sequences-in-pytorch\n",
    "        lstm_in = nn.utils.rnn.pack_padded_sequence(\n",
    "            embeds,\n",
    "            input_lens.cpu(),\n",
    "            batch_first=True,\n",
    "            enforce_sorted=False\n",
    "        )\n",
    "\n",
    "        # Pass the packed sequence through the BiLSTM\n",
    "        if hidden_states:\n",
    "            lstm_out, hidden = self.model['bilstm'](lstm_in, hidden_states)\n",
    "        else:\n",
    "            lstm_out, hidden = self.model['bilstm'](lstm_in)\n",
    "\n",
    "        # Unpack the packed sequence --> (b x sl x 2*lstm_dim)\n",
    "        lstm_out, lengths = nn.utils.rnn.pad_packed_sequence(lstm_out, batch_first=True)\n",
    "\n",
    "        # Get logits (b x seq_len x n_classes)\n",
    "        logits = self.model['ff'](lstm_out)\n",
    "        outputs = (logits, lengths)\n",
    "        if labels is not None:\n",
    "            loss = self.loss(logits.reshape(-1, self.n_classes), labels.reshape(-1))\n",
    "            outputs =  outputs + (loss,)\n",
    "\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "oH_92rb8VvEd"
   },
   "outputs": [],
   "source": [
    "def train(\n",
    "    model: nn.Module, \n",
    "    train_dl: DataLoader, \n",
    "    valid_dl: DataLoader, \n",
    "    optimizer: torch.optim.Optimizer, \n",
    "    n_epochs: int, \n",
    "    device: torch.device,\n",
    "    scheduler=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    The main training loop which will optimize a given model on a given dataset\n",
    "    :param model: The model being optimized\n",
    "    :param train_dl: The training dataset\n",
    "    :param valid_dl: A validation dataset\n",
    "    :param optimizer: The optimizer used to update the model parameters\n",
    "    :param n_epochs: Number of epochs to train for\n",
    "    :param device: The device to train on\n",
    "    :return: (model, losses) The best model and the losses per iteration\n",
    "    \"\"\"\n",
    "\n",
    "  # Keep track of the loss and best accuracy\n",
    "    losses = []\n",
    "    learning_rates = []\n",
    "    best_f1 = 0.0\n",
    "\n",
    "    # Iterate through epochs\n",
    "    for ep in range(n_epochs):\n",
    "\n",
    "        loss_epoch = []\n",
    "\n",
    "        #Iterate through each batch in the dataloader\n",
    "        for batch in tqdm(train_dl):\n",
    "            # VERY IMPORTANT: Make sure the model is in training mode, which turns on \n",
    "            # things like dropout and layer normalization\n",
    "            model.train()\n",
    "\n",
    "            # VERY IMPORTANT: zero out all of the gradients on each iteration -- PyTorch\n",
    "            # keeps track of these dynamically in its computation graph so you need to explicitly\n",
    "            # zero them out\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # Place each tensor on the GPU\n",
    "            batch = tuple(t.to(device) for t in batch)\n",
    "            input_ids = batch[0]\n",
    "            seq_lens = batch[1]\n",
    "            labels = batch[2]\n",
    "\n",
    "            # Pass the inputs through the model, get the current loss and logits\n",
    "            logits, lengths, loss = model(input_ids, seq_lens, labels=labels)\n",
    "            losses.append(loss.item())\n",
    "            loss_epoch.append(loss.item())\n",
    "\n",
    "            # Calculate all of the gradients and weight updates for the model\n",
    "            loss.backward()\n",
    "\n",
    "            # Optional: clip gradients\n",
    "            #torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "            # Finally, update the weights of the model\n",
    "            optimizer.step()\n",
    "            if scheduler != None:\n",
    "                scheduler.step()\n",
    "                learning_rates.append(scheduler.get_last_lr()[0])\n",
    "\n",
    "        # Perform inline evaluation at the end of the epoch\n",
    "        f1 = evaluate(model, valid_dl)\n",
    "        print(f'Validation F1: {f1}, train loss: {sum(loss_epoch) / len(loss_epoch)}')\n",
    "\n",
    "        # Keep track of the best model based on the accuracy\n",
    "        if f1 > best_f1:\n",
    "            torch.save(model.state_dict(), 'best_model')\n",
    "            best_f1 = f1\n",
    "\n",
    "    return losses, learning_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "LQkyUeyhV1D3"
   },
   "outputs": [],
   "source": [
    "def evaluate(model: nn.Module, valid_dl: DataLoader):\n",
    "    \"\"\"\n",
    "    Evaluates the model on the given dataset\n",
    "    :param model: The model under evaluation\n",
    "    :param valid_dl: A `DataLoader` reading validation data\n",
    "    :return: The accuracy of the model on the dataset\n",
    "    \"\"\"\n",
    "    # VERY IMPORTANT: Put your model in \"eval\" mode -- this disables things like \n",
    "    # layer normalization and dropout\n",
    "    model.eval()\n",
    "    labels_all = []\n",
    "    preds_all = []\n",
    "\n",
    "    # ALSO IMPORTANT: Don't accumulate gradients during this process\n",
    "    with torch.no_grad():\n",
    "        for batch in tqdm(valid_dl, desc='Evaluation'):\n",
    "            batch = tuple(t.to(device) for t in batch)\n",
    "            input_ids = batch[0]\n",
    "            seq_lens = batch[1]\n",
    "            labels = batch[2]\n",
    "            hidden_states = None\n",
    "\n",
    "            logits, _, _ = model(input_ids, seq_lens, hidden_states=hidden_states, labels=labels)\n",
    "            preds_all.extend(torch.argmax(logits, dim=-1).reshape(-1).detach().cpu().numpy())\n",
    "            labels_all.extend(labels.reshape(-1).detach().cpu().numpy())\n",
    "\n",
    "    P, R, F1, _ = precision_recall_fscore_support(labels_all, preds_all, average='macro')\n",
    "    print(confusion_matrix(labels_all, preds_all))\n",
    "    return F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "ycIjTfhBZGNJ"
   },
   "outputs": [],
   "source": [
    "lstm_dim = 128\n",
    "dropout_prob = 0.1\n",
    "batch_size = 8\n",
    "lr = 1e-2\n",
    "n_epochs = 10\n",
    "n_workers = 0  # set to a larger number if you run your code in colab\n",
    "\n",
    "device = torch.device(\"cpu\")\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "\n",
    "# Create the model\n",
    "model = BiLSTM(\n",
    "    pretrained_embeddings=torch.FloatTensor(pretrained_embeddings), \n",
    "    lstm_dim=lstm_dim, \n",
    "    dropout_prob=dropout_prob, \n",
    "    n_classes=len(datasets[\"train\"].features[f\"ner_tags\"].feature.names)\n",
    "  ).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "afc43fa359df4352ba07ba5e5d00054d",
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      "2614645b079b49bea5d51d1f8d99a38f",
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      "ddd1301e94394e62bacf916ad4880292",
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      "a6d38f7eb3cf43b58c5335f3b5557ff6",
      "969beef0e2184558a1c741abc45e021b"
     ]
    },
    "id": "xOdf3IBNV4hx",
    "outputId": "5b93d989-a102-4195-d7ec-88af05f514a9"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "5f809429d2864a5583df712280a659ae",
       "version_major": 2,
       "version_minor": 0
      },
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     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40b91a1be31a49e1908d48f429ed5b43",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345419     47     10     49     52     14      2     41     13]\n",
      " [    23   1789      5      1      0     20      0      3      1]\n",
      " [    25     16   1260      0      4      0      0      1      1]\n",
      " [    37     82      0   1135     27     44      0     16      0]\n",
      " [    32      5     11      8    678      2     10      1      4]\n",
      " [    25     12      0     34      8   1741      3     14      0]\n",
      " [     6      0      0      0     37      2    211      0      1]\n",
      " [    65     17      0     34      3     22      1    775      5]\n",
      " [    47      1      5      0     51      1      5     16    220]]\n",
      "Validation F1: 0.8935076155280685, train loss: 0.14170506390146825\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9905795cb97e465db4761a99e8e9c8fa",
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    {
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       "model_id": "9c73b8e5df714dde91128829b89940bc",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345436     62      2     45     22     11      2     33     34]\n",
      " [    31   1781      5      4      0     14      0      6      1]\n",
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      " [    43     17      0     35      1      6      0    813      7]\n",
      " [    27      2      5      0     16      2      0     18    276]]\n",
      "Validation F1: 0.9018831260482847, train loss: 0.02657376601092108\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345456     29     14     48     20     20      0     37     23]\n",
      " [    27   1752     18     20      1     20      0      4      0]\n",
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      " [    41     12      0     47      3      8      0    793     18]\n",
      " [    27      1      6      1     19      2      0      7    283]]\n",
      "Validation F1: 0.9104821085939905, train loss: 0.017478332217572713\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "168d2049bcfe4ccea1e2112caa756dae",
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    {
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       "model_id": "c55f08461a6c4e77aacecf939756973b",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345508     31      3     37     10     10      1     23     24]\n",
      " [    23   1794      7      7      0      7      0      3      1]\n",
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      " [    75     12      0     28      1      6      0    795      5]\n",
      " [    32      1      5      0     12      2      2     15    277]]\n",
      "Validation F1: 0.9107918478008801, train loss: 0.012492643622691443\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a813eafb1e7440f180c043c92b3abcf6",
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       "model_id": "cafc279d8b75492a84aa58268cf67bc7",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345519     20      2     40     25     10      0     21     10]\n",
      " [    29   1776      6     13      2     13      0      2      1]\n",
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      " [    62     10      0     33      3      8      0    801      5]\n",
      " [    30      1      5      3     24      2      1     12    268]]\n",
      "Validation F1: 0.9245278623743088, train loss: 0.008772710909356139\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce55418c435649b0b711e3eab634cfee",
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       "model_id": "4c19c8b62e7b4889bea4d7f06fea8aa4",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345475     33      2     25     41     20      1     36     14]\n",
      " [    25   1782      3      4      2     21      0      4      1]\n",
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      " [    62     13      0     26      3     14      0    792     12]\n",
      " [    30      1      6      2     16      1      5     20    265]]\n",
      "Validation F1: 0.9140443357471715, train loss: 0.00545991696425629\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "100b60679e3e4ec296f59634506868c8",
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       "model_id": "2aeff848b84b49fd9e48539bab9079ee",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345466     14      2     56     29     14      2     40     24]\n",
      " [    25   1767      2     23      1     18      1      4      1]\n",
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      " [    47      8      0     39      3      8      0    811      6]\n",
      " [    28      2      5      2     19      2      3     11    274]]\n",
      "Validation F1: 0.9206964790962123, train loss: 0.0035528619892963883\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77a2c7abe0de4abf9ac555ae74887f9a",
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    {
     "data": {
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       "model_id": "3b221478ebb549c6aa349c3ac900d14f",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345500     35      3     28     10     19      2     29     21]\n",
      " [    19   1794      2      7      1     14      0      4      1]\n",
      " [    22     12   1262      1      5      0      2      1      2]\n",
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      " [    55     16      0     27      3     13      0    801      7]\n",
      " [    29      1      4      1      9      2      8     13    279]]\n",
      "Validation F1: 0.9228745320291222, train loss: 0.0022250179169699172\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74b69543d1f4401381a3d4ae19aa46d7",
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       "model_id": "a7aa4d79afe240c1bbf648e6e6b4421f",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345500     28      2     41     23     10      2     23     18]\n",
      " [    21   1784      2     15      1     13      0      5      1]\n",
      " [    21     10   1262      2      6      0      1      1      4]\n",
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      " [    53     11      0     31      3     10      0    808      6]\n",
      " [    30      0      4      1     21      1      3     14    272]]\n",
      "Validation F1: 0.9249685641215538, train loss: 0.0012310007892623906\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2c9529840fd4509bb55be62b7e30bbf",
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     "metadata": {},
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a368684d8f064cb2aa64e61d8d87c937",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[345508     24      2     37     19     13      2     24     18]\n",
      " [    24   1780      2     14      1     15      0      5      1]\n",
      " [    20      9   1264      2      5      0      2      1      4]\n",
      " [    31     35      0   1220     12     26      1     16      0]\n",
      " [    41      0     12      8    665      7      7      1     10]\n",
      " [     7      4      0     43      3   1764      2     14      0]\n",
      " [    10      0      0      0     10      2    232      0      3]\n",
      " [    53     11      0     31      3     10      0    808      6]\n",
      " [    29      0      4      1     18      1      4     18    271]]\n",
      "Validation F1: 0.925124536751276, train loss: 0.0008371544579220439\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dl = DataLoader(datasets['train'], batch_size=batch_size, shuffle=True, collate_fn=collate_batch_bilstm, num_workers=n_workers)\n",
    "valid_dl = DataLoader(datasets['validation'], batch_size=len(datasets['validation']), collate_fn=collate_batch_bilstm, num_workers=n_workers)\n",
    "\n",
    "# Create the optimizer\n",
    "optimizer = Adam(model.parameters(), lr=lr)\n",
    "scheduler = CyclicLR(optimizer, base_lr=0., max_lr=lr, step_size_up=1, step_size_down=len(train_dl)*n_epochs, cycle_momentum=False)\n",
    "\n",
    "# Train\n",
    "losses, learning_rates = train(model, train_dl, valid_dl, optimizer, n_epochs, device, scheduler)\n",
    "model.load_state_dict(torch.load('best_model'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning rate schedules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Motivation: \n",
    "- speed up training\n",
    "- to train a better model\n",
    "\n",
    "With Pytorch:\n",
    "- choose a learning rate schedulers form `torch.optim.lr_schedule`\n",
    "- add a line in your training loop which calls the `step()` function of your scheduler\n",
    "- this will automatically change your learning rate! \n",
    "- **Note**: be aware of when to call `step()`; some schedulers change the learning rate after every epoch, and some change after every training step (batch). The one we will use here changes the learning rate after every training step. We'll define the scheduler in the cell that calls the `train()` function. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up hyperparameters and create the model. Note the high learning rate -- this is partially due to the learning rate scheduler we will use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we have used the `CyclicLR` scheduler. The cyclic learning rate schedule in general looks like this:\n",
    "\n",
    "![](https://ai-how.github.io/img/CLR.png) [Source](https://arxiv.org/pdf/1506.01186.pdf)\n",
    "\n",
    "We are using it here to linearly decay the learning rate from a starting max learning rate (here 1e-2) down to 0 over the entire course of training (essentially one cycle that starts at the max and ends at 0). \n",
    "\n",
    "\" Allowing the learning rate to rise and fall is beneficial overall\n",
    "even though it might temporarily harm the network’s performance\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f75e401cd50>]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f75e54d1790>]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(learning_rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328,
     "referenced_widgets": [
      "6cdc774b00ab477d956c98d936c2a422"
     ]
    },
    "id": "RWxwYR7KV-RA",
    "outputId": "15ce76cc-1373-4dde-88ed-bca45b3bd1b9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7af82e38e7e94395b38173cdb0084ba9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[419531     57     20    157     74     86     12     76     47]\n",
      " [   113   1363     22     73      9     33      2      1      1]\n",
      " [    87      9   1042      2     13      1      2      0      0]\n",
      " [   116     27      2   1364     27     89      0     33      3]\n",
      " [    55      1      9     11    703      5     33      4     14]\n",
      " [    58     18      0     51      3   1502      4     31      1]\n",
      " [    27      0      6      0     14      7    202      0      1]\n",
      " [    71      9      1     37      6     16      0    557      5]\n",
      " [    32      0      3      1     14      3      8      6    149]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8411122480145246"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dl = DataLoader(datasets['test'], batch_size=len(datasets['test']), collate_fn=collate_batch_bilstm, num_workers=n_workers)\n",
    "\n",
    "evaluate(model, test_dl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YATCbeeTDNQG"
   },
   "source": [
    "# Beam Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sylZvVBm0lo_"
   },
   "source": [
    "![](https://miro.medium.com/max/700/1*tEjhWqUgjX37VnT7gJN-4g.png) [(source)](https://towardsdatascience.com/foundations-of-nlp-explained-visually-beam-search-how-it-works-1586b9849a24)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "oKUr7j_y1J4p"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4, 0, 4, 0, 4, 0, 4, 0, 4, 0], 6.931471805599453]\n",
      "[[4, 0, 4, 0, 4, 0, 4, 0, 4, 1], 7.154615356913663]\n",
      "[[4, 0, 4, 0, 4, 0, 4, 0, 3, 0], 7.154615356913663]\n"
     ]
    }
   ],
   "source": [
    "# source https://machinelearningmastery.com/beam-search-decoder-natural-language-processing/\n",
    "\n",
    "def beam_search_decoder(data, k):\n",
    "    sequences = [[list(), 0.0]]\n",
    "    # walk over each step in sequence\n",
    "    for row in data:\n",
    "        all_candidates = list()\n",
    "        # expand each current candidate\n",
    "        for i in range(len(sequences)):\n",
    "            seq, score = sequences[i]\n",
    "            for j in range(len(row)):\n",
    "                candidate = [seq + [j], score - log(row[j])]\n",
    "                all_candidates.append(candidate)\n",
    "        # order all candidates by score\n",
    "        ordered = sorted(all_candidates, key=lambda tup:tup[1])\n",
    "        # select k best\n",
    "        sequences = ordered[:k]\n",
    "    return sequences\n",
    " \n",
    "# define a sequence of 10 words over a vocab of 5 words\n",
    "data = [[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "        [0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "        [0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "        [0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "        [0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "        [0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "        [0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "        [0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "        [0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "        [0.5, 0.4, 0.3, 0.2, 0.1]]\n",
    "data = array(data)\n",
    "# decode sequence\n",
    "result = beam_search_decoder(data, 3)\n",
    "# print result\n",
    "for seq in result:\n",
    "    print(seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lOTwIiaG1KPU"
   },
   "source": [
    "- **Question: Can you find what is the problem with the above?**\n",
    "\n",
    "\n",
    "- In the above, when generating text, the probability distribution for the next step does not depend on the previous step's choice.\n",
    "- Beam search is usually employed with encoder-decoder architectures:\n",
    "![](https://miro.medium.com/max/700/1*GkG_5wg57IpkU8F84nJubQ.png)\n",
    "- At each step, the decoder receives as input the prediction of the previous step and the hidden state of the previous step.\n",
    "- During training : at each step provide either the prediction at the previous step with highest probability or the gold label for the next step (teacher forcing).\n",
    "- During testing: build a beam of top-k generated sequences and re-run the decoder with each of them.\n",
    "\n",
    "Resources:\n",
    "- Implementing an encoder-decoder model [example 1](https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html), [example 2](https://bastings.github.io/annotated_encoder_decoder/)\n",
    "- Implementing beam search [example](https://github.com/budzianowski/PyTorch-Beam-Search-Decoding/blob/master/decode_beam.py)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "aUlZdPe7v5KJ"
   },
   "outputs": [],
   "source": [
    "class EncoderRNN(nn.Module):\n",
    "    \"\"\"\n",
    "    RNN Encoder model.\n",
    "    \"\"\"\n",
    "    def __init__(self, \n",
    "            pretrained_embeddings: torch.tensor, \n",
    "            lstm_dim: int,\n",
    "            dropout_prob: float = 0.1):\n",
    "        \"\"\"\n",
    "        Initializer for EncoderRNN network\n",
    "        :param pretrained_embeddings: A tensor containing the pretrained embeddings\n",
    "        :param lstm_dim: The dimensionality of the LSTM network\n",
    "        :param dropout_prob: Dropout probability\n",
    "        \"\"\"\n",
    "        # First thing is to call the superclass initializer\n",
    "        super(EncoderRNN, self).__init__()\n",
    "\n",
    "        # We'll define the network in a ModuleDict, which makes organizing the model a bit nicer\n",
    "        # The components are an embedding layer, and an LSTM layer.\n",
    "        self.model = nn.ModuleDict({\n",
    "            'embeddings': nn.Embedding.from_pretrained(pretrained_embeddings, padding_idx=pretrained_embeddings.shape[0] - 1),\n",
    "            'lstm': nn.LSTM(pretrained_embeddings.shape[1], lstm_dim, 2, batch_first=True, bidirectional=True),\n",
    "        })\n",
    "        # Initialize the weights of the model\n",
    "        self._init_weights()\n",
    "\n",
    "    def _init_weights(self):\n",
    "        all_params = list(self.model['lstm'].named_parameters())\n",
    "        for n, p in all_params:\n",
    "            if 'weight' in n:\n",
    "                nn.init.xavier_normal_(p)\n",
    "            elif 'bias' in n:\n",
    "                nn.init.zeros_(p)\n",
    "\n",
    "    def forward(self, inputs, input_lens):\n",
    "        \"\"\"\n",
    "        Defines how tensors flow through the model\n",
    "        :param inputs: (b x sl) The IDs into the vocabulary of the input samples\n",
    "        :param input_lens: (b) The length of each input sequence\n",
    "        :return: (lstm output state, lstm hidden state) \n",
    "        \"\"\"\n",
    "        embeds = self.model['embeddings'](inputs)\n",
    "        lstm_in = nn.utils.rnn.pack_padded_sequence(\n",
    "                    embeds,\n",
    "                    input_lens.cpu(),\n",
    "                    batch_first=True,\n",
    "                    enforce_sorted=False\n",
    "                )\n",
    "        lstm_out, hidden_states = self.model['lstm'](lstm_in)\n",
    "        lstm_out, _ = nn.utils.rnn.pad_packed_sequence(lstm_out, batch_first=True)\n",
    "        return lstm_out, hidden_states\n",
    "\n",
    "\n",
    "class DecoderRNN(nn.Module):\n",
    "    \"\"\"\n",
    "    RNN Decoder model.\n",
    "    \"\"\"\n",
    "    def __init__(self, pretrained_embeddings: torch.tensor, \n",
    "            lstm_dim: int,\n",
    "            dropout_prob: float = 0.1,\n",
    "            n_classes: int = 2):\n",
    "        \"\"\"\n",
    "        Initializer for DecoderRNN network\n",
    "        :param pretrained_embeddings: A tensor containing the pretrained embeddings\n",
    "        :param lstm_dim: The dimensionality of the LSTM network\n",
    "        :param dropout_prob: Dropout probability\n",
    "        :param n_classes: Number of prediction classes\n",
    "        \"\"\"\n",
    "        # First thing is to call the superclass initializer\n",
    "        super(DecoderRNN, self).__init__()\n",
    "        # We'll define the network in a ModuleDict, which makes organizing the model a bit nicer\n",
    "        # The components are an embedding layer, a LSTM layer, and a feed-forward output layer\n",
    "        self.model = nn.ModuleDict({\n",
    "            'embeddings': nn.Embedding.from_pretrained(pretrained_embeddings, padding_idx=pretrained_embeddings.shape[0] - 1),\n",
    "            'lstm': nn.LSTM(pretrained_embeddings.shape[1], lstm_dim, 2, bidirectional=True, batch_first=True),\n",
    "            'nn': nn.Linear(lstm_dim*2, n_classes),\n",
    "        })\n",
    "        # Initialize the weights of the model\n",
    "        self._init_weights()      \n",
    "\n",
    "    def forward(self, inputs, hidden, input_lens):\n",
    "        \"\"\"\n",
    "        Defines how tensors flow through the model\n",
    "        :param inputs: (b x sl) The IDs into the vocabulary of the input samples\n",
    "        :param hidden: (b) The hidden state of the previous step\n",
    "        :param input_lens: (b) The length of each input sequence\n",
    "        :return: (output predictions, lstm hidden states) the hidden states will be used as input at the next step\n",
    "        \"\"\"\n",
    "        embeds = self.model['embeddings'](inputs)\n",
    "\n",
    "        lstm_in = nn.utils.rnn.pack_padded_sequence(\n",
    "                    embeds,\n",
    "                    input_lens.cpu(),\n",
    "                    batch_first=True,\n",
    "                    enforce_sorted=False\n",
    "                )\n",
    "        lstm_out, hidden_states = self.model['lstm'](lstm_in, hidden)\n",
    "        lstm_out, _ = nn.utils.rnn.pad_packed_sequence(lstm_out, batch_first=True)\n",
    "        output = self.model['nn'](lstm_out)\n",
    "        return output, hidden_states\n",
    "\n",
    "    def _init_weights(self):\n",
    "        all_params = list(self.model['lstm'].named_parameters()) + list(self.model['nn'].named_parameters())\n",
    "        for n, p in all_params:\n",
    "            if 'weight' in n:\n",
    "                nn.init.xavier_normal_(p)\n",
    "            elif 'bias' in n:\n",
    "                nn.init.zeros_(p)\n",
    "\n",
    "# Define the model\n",
    "class Seq2Seq(nn.Module):\n",
    "    \"\"\"\n",
    "    Basic Seq2Seq network\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "            self,\n",
    "            pretrained_embeddings: torch.tensor,\n",
    "            lstm_dim: int,\n",
    "            dropout_prob: float = 0.1,\n",
    "            n_classes: int = 2\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Initializer for basic Seq2Seq network\n",
    "        :param pretrained_embeddings: A tensor containing the pretrained embeddings\n",
    "        :param lstm_dim: The dimensionality of the LSTM network\n",
    "        :param dropout_prob: Dropout probability\n",
    "        :param n_classes: The number of output classes\n",
    "        \"\"\"\n",
    "\n",
    "        # First thing is to call the superclass initializer\n",
    "        super(Seq2Seq, self).__init__()\n",
    "\n",
    "        # We'll define the network in a ModuleDict, which consists of an encoder and a decoder\n",
    "        self.model = nn.ModuleDict({\n",
    "            'encoder': EncoderRNN(pretrained_embeddings, lstm_dim, dropout_prob),\n",
    "            'decoder': DecoderRNN(pretrained_embeddings, lstm_dim, dropout_prob, n_classes),\n",
    "        })\n",
    "        self.loss = nn.CrossEntropyLoss(weight=torch.FloatTensor([0.5]+[1]*(len(datasets[\"train\"].features[f\"ner_tags\"].feature.names)-1)).to(device))\n",
    "\n",
    "\n",
    "    def forward(self, inputs, input_lens, labels=None):\n",
    "        \"\"\"\n",
    "        Defines how tensors flow through the model. \n",
    "        For the Seq2Seq model this includes 1) encoding the whole input text, \n",
    "        and running *target_length* decoding steps to predict the tag of each token.\n",
    "\n",
    "        :param inputs: (b x sl) The IDs into the vocabulary of the input samples\n",
    "        :param input_lens: (b) The length of each input sequence\n",
    "        :param labels: (b) The label of each sample\n",
    "        :return: (loss, logits) if `labels` is not None, otherwise just (logits,)\n",
    "        \"\"\"\n",
    "\n",
    "        # Get embeddings (b x sl x embedding dim)\n",
    "        encoder_output, encoder_hidden = self.model['encoder'](inputs, input_lens)\n",
    "        decoder_hidden = encoder_hidden\n",
    "        decoder_input = torch.tensor([tokenizer.encode(['[BOS]'])]*inputs.shape[0], device=device)\n",
    "        target_length = labels.size(1)\n",
    "\n",
    "        loss = None\n",
    "        for di in range(target_length):\n",
    "            decoder_output, decoder_hidden = self.model['decoder'](\n",
    "                decoder_input, decoder_hidden, torch.tensor([1]*inputs.shape[0]))\n",
    "\n",
    "            if loss == None:   \n",
    "                loss = self.loss(decoder_output.squeeze(1), labels[:, di])\n",
    "            else:\n",
    "                loss += self.loss(decoder_output.squeeze(1), labels[:, di])\n",
    "            # Teacher forcing: Feed the target as the next input\n",
    "            decoder_input = labels[:, di].unsqueeze(-1) \n",
    "\n",
    "        return loss / target_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "2OAd-kev7BcY"
   },
   "outputs": [],
   "source": [
    "def train(\n",
    "    model: nn.Module, \n",
    "    train_dl: DataLoader, \n",
    "    valid_dl: DataLoader, \n",
    "    optimizer: torch.optim.Optimizer, \n",
    "    n_epochs: int, \n",
    "    device: torch.device,\n",
    "):\n",
    "    \"\"\"\n",
    "    The main training loop which will optimize a given model on a given dataset\n",
    "    :param model: The model being optimized\n",
    "    :param train_dl: The training dataset\n",
    "    :param valid_dl: A validation dataset\n",
    "    :param optimizer: The optimizer used to update the model parameters\n",
    "    :param n_epochs: Number of epochs to train for\n",
    "    :param device: The device to train on\n",
    "    :return: (model, losses) The best model and the losses per iteration\n",
    "    \"\"\"\n",
    "\n",
    "    # Keep track of the loss and best accuracy\n",
    "    losses = []\n",
    "    best_f1 = 0.0\n",
    "\n",
    "    # Iterate through epochs\n",
    "    for ep in range(n_epochs):\n",
    "\n",
    "        loss_epoch = []\n",
    "\n",
    "        #Iterate through each batch in the dataloader\n",
    "        for batch in tqdm(train_dl):\n",
    "            # VERY IMPORTANT: Make sure the model is in training mode, which turns on \n",
    "            # things like dropout and layer normalization\n",
    "            model.train()\n",
    "\n",
    "            # VERY IMPORTANT: zero out all of the gradients on each iteration -- PyTorch\n",
    "            # keeps track of these dynamically in its computation graph so you need to explicitly\n",
    "            # zero them out\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # Place each tensor on the GPU\n",
    "            batch = tuple(t.to(device) for t in batch)\n",
    "            input_ids = batch[0]\n",
    "            labels = batch[2]\n",
    "            input_lens = batch[1]\n",
    "\n",
    "            # Pass the inputs through the model, get the current loss and logits\n",
    "            loss = model(input_ids, labels=labels, input_lens=input_lens)\n",
    "            losses.append(loss.item())\n",
    "            loss_epoch.append(loss.item())\n",
    "\n",
    "            # Calculate all of the gradients and weight updates for the model\n",
    "            loss.backward()\n",
    "\n",
    "            # Optional: clip gradients\n",
    "            #torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "            # Finally, update the weights of the model\n",
    "            optimizer.step()\n",
    "\n",
    "        # Perform inline evaluation at the end of the epoch\n",
    "        f1 = evaluate(model, valid_dl)\n",
    "        print(f'Validation F1: {f1}, train loss: {sum(loss_epoch) / len(loss_epoch)}')\n",
    "\n",
    "        # Keep track of the best model based on the accuracy\n",
    "        if f1 > best_f1:\n",
    "            torch.save(model.state_dict(), 'best_model')\n",
    "            best_f1 = f1\n",
    "\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "OykuF5dURSd5"
   },
   "outputs": [],
   "source": [
    "softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "def decode(model, inputs, input_lens, labels=None, beam_size=2):\n",
    "    \"\"\"\n",
    "    Decoding/predicting the labels for an input text by running beam search.\n",
    "\n",
    "    :param inputs: (b x sl) The IDs into the vocabulary of the input samples\n",
    "    :param input_lens: (b) The length of each input sequence\n",
    "    :param labels: (b) The label of each sample\n",
    "    :param beam_size: the size of the beam \n",
    "    :return: predicted sequence of labels\n",
    "    \"\"\"\n",
    "\n",
    "    assert inputs.shape[0] == 1\n",
    "    # first, encode the input text\n",
    "    encoder_output, encoder_hidden = model.model['encoder'](inputs, input_lens)\n",
    "    decoder_hidden = encoder_hidden\n",
    "\n",
    "    # the decoder starts generating after the Begining of Sentence (BOS) token\n",
    "    decoder_input = torch.tensor([tokenizer.encode(['[BOS]',]),], device=device)\n",
    "    target_length = labels.shape[1]\n",
    "    \n",
    "    # we will use heapq to keep top best sequences so far sorted in heap_queue \n",
    "    # these will be sorted by the first item in the tuple\n",
    "    heap_queue = []\n",
    "    heap_queue.append((torch.tensor(0), tokenizer.encode(['[BOS]']), decoder_input, decoder_hidden))\n",
    "\n",
    "    # Beam Decoding\n",
    "    for _ in range(target_length):\n",
    "        # print(\"next len\")\n",
    "        new_items = []\n",
    "        # for each item on the beam\n",
    "        for j in range(len(heap_queue)): \n",
    "            # 1. remove from heap\n",
    "            score, tokens, decoder_input, decoder_hidden = heapq.heappop(heap_queue)\n",
    "            # 2. decode one more step\n",
    "            decoder_output, decoder_hidden = model.model['decoder'](\n",
    "                decoder_input, decoder_hidden, torch.tensor([1]))\n",
    "            decoder_output = softmax(decoder_output)\n",
    "            # 3. get top-k predictions\n",
    "            best_idx = torch.argsort(decoder_output[0], descending=True)[0]\n",
    "            # print(decoder_output)\n",
    "            # print(best_idx)\n",
    "            for i in range(beam_size):\n",
    "                decoder_input = torch.tensor([[best_idx[i]]], device=device)\n",
    "                \n",
    "                new_items.append((score + decoder_output[0,0, best_idx[i]],\n",
    "                                  tokens + [best_idx[i].item()], \n",
    "                                  decoder_input, \n",
    "                                  decoder_hidden))\n",
    "        # add new sequences to the heap\n",
    "        for item in new_items:\n",
    "          # print(item)\n",
    "            heapq.heappush(heap_queue, item)\n",
    "        # remove sequences with lowest score (items are sorted in descending order)\n",
    "        while len(heap_queue) > beam_size:\n",
    "            heapq.heappop(heap_queue)\n",
    "          \n",
    "    final_sequence = heapq.nlargest(1, heap_queue)[0]\n",
    "    assert labels.shape[1] == len(final_sequence[1][1:])\n",
    "    return final_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "9tV1Sdlze9eO"
   },
   "outputs": [],
   "source": [
    "def evaluate(model: nn.Module, valid_dl: DataLoader, beam_size:int = 1):\n",
    "    \"\"\"\n",
    "    Evaluates the model on the given dataset\n",
    "    :param model: The model under evaluation\n",
    "    :param valid_dl: A `DataLoader` reading validation data\n",
    "    :return: The accuracy of the model on the dataset\n",
    "    \"\"\"\n",
    "    # VERY IMPORTANT: Put your model in \"eval\" mode -- this disables things like \n",
    "    # layer normalization and dropout\n",
    "    model.eval()\n",
    "    labels_all = []\n",
    "    logits_all = []\n",
    "    tags_all = []\n",
    "\n",
    "    # ALSO IMPORTANT: Don't accumulate gradients during this process\n",
    "    with torch.no_grad():\n",
    "        for batch in tqdm(valid_dl, desc='Evaluation'):\n",
    "            batch = tuple(t.to(device) for t in batch)\n",
    "            input_ids = batch[0]\n",
    "            input_lens = batch[1]\n",
    "            labels = batch[2]\n",
    "\n",
    "            best_seq = decode(model, input_ids, input_lens, labels=labels, beam_size=beam_size)\n",
    "            mask = (input_ids != 0)\n",
    "            labels_all.extend([l for seq,samp in zip(list(labels.detach().cpu().numpy()), input_ids) for l,i in zip(seq,samp) if i != 0])\n",
    "            tags_all += best_seq[1][1:]\n",
    "            # print(best_seq[1][1:], labels)\n",
    "    P, R, F1, _ = precision_recall_fscore_support(labels_all, tags_all, average='macro')\n",
    "    print(confusion_matrix(labels_all, tags_all))\n",
    "    return F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "4KjDoQkl8Omy"
   },
   "outputs": [],
   "source": [
    "lstm_dim = 300\n",
    "dropout_prob = 0.1\n",
    "batch_size = 64\n",
    "lr = 1e-3\n",
    "n_epochs = 20\n",
    "n_workers = 0\n",
    "\n",
    "device = torch.device(\"cpu\")\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "\n",
    "# Create the model\n",
    "model = Seq2Seq(\n",
    "    pretrained_embeddings=torch.FloatTensor(pretrained_embeddings), \n",
    "    lstm_dim=lstm_dim, \n",
    "    dropout_prob=dropout_prob, \n",
    "    n_classes=len(datasets[\"train\"].features[f\"ner_tags\"].feature.names)\n",
    "  ).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    },
    "id": "iHvNb7nm6kLI",
    "outputId": "0436841f-d3a3-44aa-bc76-b9eda50f2fac"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13dd97cb90cf43cfb2de15168cf9e933",
       "version_major": 2,
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      },
      "text/plain": [
       "  0%|          | 0/220 [00:00<?, ?it/s]"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Evaluation:   0%|          | 0/3250 [00:00<?, ?it/s]"
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     },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[40937   526   433   155   194   192    68    47   207]\n",
      " [  515  1052   218    15    13    14     3     2    10]\n",
      " [  379   146   758     3     9     3     3     1     5]\n",
      " [  467    49    19   686    66    32     5    10     7]\n",
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      " [  154     5     5    22    24    18    12    14    92]]\n",
      "Validation F1: 0.5148684803742811, train loss: 0.1254397713663903\n"
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      "Validation F1: 0.5992961020420663, train loss: 0.08672628991983154\n"
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      "Validation F1: 0.6389387782477018, train loss: 0.06806593586436727\n"
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      "Validation F1: 0.6429197452938862, train loss: 0.057959155670621175\n"
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      "Validation F1: 0.7031936004879823, train loss: 0.04639621409164234\n"
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      "Validation F1: 0.7135904551805614, train loss: 0.03937693921510469\n"
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      "Validation F1: 0.7209686415011278, train loss: 0.03529796007258648\n"
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      "Validation F1: 0.7763060675321695, train loss: 0.030404941890050063\n"
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      "Validation F1: 0.7765224797107156, train loss: 0.024601146790452978\n"
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      " [   67     1     8     4    32     3    14    16   201]]\n",
      "Validation F1: 0.7671100558971141, train loss: 0.021562039246782662\n"
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      " [   62     3    10     2    23     3    16    12   215]]\n",
      "Validation F1: 0.7923309583490342, train loss: 0.017838069995526562\n"
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      " [   51     1     9    10    25     1     3    17   229]]\n",
      "Validation F1: 0.7990632659575675, train loss: 0.01514953335757706\n"
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      " [   52     2     4     7    19     2     4    26   230]]\n",
      "Validation F1: 0.8022651715326824, train loss: 0.015754620486404747\n"
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      " [  149  1556    72    23     5    21     1    12     3]\n",
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      " [   58     2     8    12    27     6     9    23   201]]\n",
      "Validation F1: 0.8001482986272885, train loss: 0.013831334230913357\n"
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      " [  162    15     0    27     2    21     1   682    12]\n",
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      "Validation F1: 0.8245979034012647, train loss: 0.011220586808948692\n"
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      "Validation F1: 0.8035197642799284, train loss: 0.013202182106165724\n"
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      "Validation F1: 0.8160772657119046, train loss: 0.009503976078386503\n"
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      "Validation F1: 0.8246442255568004, train loss: 0.008092457134096714\n"
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      "Validation F1: 0.8295417224894234, train loss: 0.013141678918724541\n"
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      "[[41750   220   126   184   113   160    28   100    78]\n",
      " [  156  1528    93    27    11    16     3     8     0]\n",
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      " [  129    16     0    42     8    25     2   670    30]\n",
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      "Validation F1: 0.7906017625430706, train loss: 0.0177222935853272\n"
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    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
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   "source": [
    "train_dl = DataLoader(datasets['train'], batch_size=batch_size, shuffle=True, collate_fn=collate_batch_bilstm, num_workers=n_workers)\n",
    "valid_dl = DataLoader(datasets['validation'], batch_size=1, collate_fn=collate_batch_bilstm, num_workers=n_workers)\n",
    "\n",
    "# Create the optimizer\n",
    "optimizer = Adam(model.parameters(), lr=lr)\n",
    "\n",
    "# Train\n",
    "losses = train(model, train_dl, valid_dl, optimizer, n_epochs, device)\n",
    "model.load_state_dict(torch.load('best_model'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "**Question: Do you have ideas how to improve the model?**\n",
    "How about adding attention mechanism for the decoder to attend to the separate hidden states of the separate token steps in the encoder? (see the resources)\n"
   ]
  },
  {
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      "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  cpuset_checked))\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
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      "[[37092   237   111   313   218    97    32   133    90]\n",
      " [  188  1254    51    73    19    20     0     9     3]\n",
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    "test_dl = DataLoader(datasets['test'], batch_size=1, collate_fn=collate_batch_bilstm, num_workers=n_workers)\n",
    "evaluate(model, test_dl, beam_size=1)"
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      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
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       "Evaluation:   0%|          | 0/3453 [00:00<?, ?it/s]"
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      "  cpuset_checked))\n"
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      "[[37128   232   121   311   209    93    32   128    69]\n",
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  {
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   "source": [
    "# Transformers for sequence classification\n",
    "\n",
    "- have to adjust the vocabulary where a word is split into multiple word piesces\n",
    "- [Tutorial on NER](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb)\n",
    "- Some generative transformers now perform the same as token classification transformers [e.g. T5 can extract the span of a tweet that contains a sentiment](https://github.com/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb)"
   ]
  }
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