{
"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, and pytorch-crf to add a CRF to our BiLSTM."
]
},
{
"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": [
"Requirement already satisfied: fasttext in /Users/knf792/miniconda3/envs/nlp-course/lib/python3.10/site-packages (0.9.2)\n",
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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",
"Collecting threadpoolctl>=2.0.0\n",
" Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\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 pytorch-crf\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 torchcrf import CRF\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, load_metric\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",
"\n",
" - which tokens in a news article contain propagandistic techniques\n",
"\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",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 569,
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},
"id": "BoEPDmb6QTw5",
"outputId": "b4e2e71c-63f8-46f2-ae37-f4a3957850a1"
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{
"name": "stdout",
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"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"
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},
"text/plain": [
"Generating train split: 0%| | 0/14041 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating validation split: 0%| | 0/3250 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating test split: 0%| | 0/3453 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"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",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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",
"95164b73146c4484873e302a514db3cb",
"00fdcbd96cb94d3eb4ebce2dd65453df",
"69dac05df03a4505bf57ddee27c052c7",
"5230116d8f9d40fbb22be74eb6bddba8",
"04e7a1a51fc843a28998e9c233eb6b53",
"ed85ba97e4db4aa4870c97fb5dae9320",
"032c119a998a43efa68e584df3832d19",
"7afee097ce0d4179a71382e1a770347d",
"bba4178aa4d44e9d91190d821d05b7ee",
"2614645b079b49bea5d51d1f8d99a38f",
"f01071efe73b4151a3b75e73dd249c48",
"b314e1ca91b1434e951253e9bebf6b0b",
"0258bc4f1039499f8d27f95ea6881d4b",
"470e65e908714a76a9a1aaeb9b889b62",
"ca708c2a421b4dd9a744970e3d9ae610",
"70a96efaef804fd8ad5ce058b130a5d8",
"0dedc3af2f79435a8f866cd47b0258ff",
"47d6e470ee324f50aa0b2ff7539a5308",
"1d004bcd0131429296f108282d445751",
"5e341b1c7e5346caad69ba22a54ec4dc",
"dc5d256cae234801877fb9ce3ca792cf",
"8ce8e901bde44dd6bf7de4ca40f35a5b",
"e1c62c4d464b451ebf33b77b2e8dbba6",
"931f034c69de4c379d851d93eb1a9084",
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"bfcbe3d471ad412ca4161ee4fbb4125d",
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" [ 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"
]
},
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"Validation F1: 0.9018831260482847, train loss: 0.02657376601092108\n"
]
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"Validation F1: 0.9104821085939905, train loss: 0.017478332217572713\n"
]
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"Validation F1: 0.9107918478008801, train loss: 0.012492643622691443\n"
]
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"Validation F1: 0.9245278623743088, train loss: 0.008772710909356139\n"
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"Validation F1: 0.9140443357471715, train loss: 0.00545991696425629\n"
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"Validation F1: 0.9206964790962123, train loss: 0.0035528619892963883\n"
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"text": [
"[[345500 35 3 28 10 19 2 29 21]\n",
" [ 19 1794 2 7 1 14 0 4 1]\n",
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"Validation F1: 0.9228745320291222, train loss: 0.0022250179169699172\n"
]
},
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"Validation F1: 0.9249685641215538, train loss: 0.0012310007892623906\n"
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"[[345508 24 2 37 19 13 2 24 18]\n",
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"Validation F1: 0.925124536751276, train loss: 0.0008371544579220439\n"
]
},
{
"data": {
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"<All keys matched successfully>"
]
},
"execution_count": 78,
"metadata": {},
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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=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": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328,
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},
"id": "RWxwYR7KV-RA",
"outputId": "15ce76cc-1373-4dde-88ed-bca45b3bd1b9"
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"[[419531 57 20 157 74 86 12 76 47]\n",
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"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": "89C8rnG3UCgo"
},
"source": [
"# Conditional Random Field (CRF) \n",
"\n",
"\n",
"\n",
"- learn from and perform inference on data whose predictions depend on each other\n",
"- a type of graphical model \n",
"- **nodes** are the individual observations you wish to make predictions on \n",
"- **edges** are dependencies between the nodes\n",
"- the prediction you make for one token can change your belief about the other tokens\n",
" - after determiners (DT), adjectives and nouns are much more likely than verbs\n",
"- unfortunately, trying to model the dependencies between each node in an arbitrarily sized graph is combinatorial and thus intractable, so we have to make some simplifying assumptions.\n",
"\n",
"Assumptions for a **linear chain CRF**:\n",
"- assume that your data is structured as a sequence\n",
"- assume that your prediction at time $t$ is only dependent on your prediction at time $t - 1$\n",
"- you make predictions by modeling two things:\n",
" - the **probability of a label given your input** ($p(y_{t}|X)$)\n",
" - the **probability of a label given the previous label** ($p(y_t|y_{t-1})$).\n",
"\n",
"In the **BiLSTM-CRF**\n",
"- input probabilities $p(y_t|X)$ are modeled using the BiLSTM (as usual)\n",
"- the probabilities $p(y_t|y_{t-1})$ are modeled using a transition matrix $V$ of size $n \\times n$ where $n$ is the number of tags (i.e., one transition probability for each possible transition). \n",
"- in practice - add a CRF on top of your BiLSTM output logits instead of using a softmax and cross-entropy on the BiLSTM logits.\n",
"\n",
"\n",
"[Source](https://www.aclweb.org/anthology/N16-1030.pdf)\n",
"\n",
"The model is then trained by maximizing the log-likelihood (i.e. minimizing the negative log-likelihood) of the entire sequence. For more in depth explanation of how this is performed, see the lectures from Hugo Larochelle [here](https://www.youtube.com/watch?v=PGBlyKtfB74&ab_channel=HugoLarochelle).\n",
"\n",
"\n",
"Only a few lines of code to add a CRF using this third party library: [pytorch-crf](https://pytorch-crf.readthedocs.io/en/stable/). For a more advanced implementation, check [AllenNLP CRF module](https://github.com/allenai/allennlp/blob/master/allennlp/modules/conditional_random_field.py)\n",
"\n",
"The differences are:\n",
"\n",
"- Instead of taking a softmax/cross-entropy loss using the logits from the BiLSTM, we pass the logits to the pytorch-crf CRF module. The output of this model is the **log-likelihood of the entire sequence** (for each sequence in the batch). Since our objective is to minimize the loss, we take the **negative** of the log likelihood as our loss.\n",
"- There is now a **decode** function, which passes logits through the CRF to get the most likely tag sequences.\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "_X3lSEtTJ711"
},
"outputs": [],
"source": [
"# Define the model\n",
"class BiLSTM_CRF(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_CRF, 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],\n",
" lstm_dim,\n",
" 2,\n",
" batch_first=True,\n",
" dropout=dropout_prob,\n",
" bidirectional=True),\n",
" 'ff': nn.Linear(2*lstm_dim, n_classes),\n",
" 'CRF': CRF(n_classes, batch_first=True)\n",
" })\n",
" self.n_classes = n_classes\n",
"\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, 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\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",
" lstm_out, hidden = self.model['bilstm'](lstm_in)\n",
"\n",
" # Unpack the packed sequence --> (b x sl x 2*lstm_dim)\n",
" lstm_out,_ = nn.utils.rnn.pad_packed_sequence(lstm_out, batch_first=True)\n",
"\n",
" # Get emissions (b x seq_len x n_classes)\n",
" emissions = self.model['ff'](lstm_out)\n",
" outputs = (emissions,)\n",
" if labels is not None:\n",
" mask = (inputs != 0)\n",
" # log-likelihood from the CRF\n",
" log_likelihood = self.model['CRF'](emissions, labels, mask=mask, reduction='token_mean')\n",
" outputs = (-log_likelihood,) + outputs\n",
"\n",
" return outputs\n",
"\n",
" def decode(self, emissions, mask):\n",
" \"\"\"\n",
" Given a set of emissions and a mask, decode the sequence\n",
" \"\"\"\n",
" return self.model['CRF'].decode(emissions, mask=mask)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zQVER0rN-31W"
},
"source": [
"## Traning the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4IRd6wlkhbc2"
},
"source": [
"The evaluation function is also slightly different -- we evaluate perfomance based on the decoded sequence from the CRF as opposed to the output of the BiLSTM. We use macro-F1 score for this."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "9lmOV4WxOSLw"
},
"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",
" loss, logits = 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",
" #gc.collect()\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",
" #gc.collect()\n",
"\n",
" return losses, learning_rates"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"id": "cF45DpLPNmF4"
},
"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",
" 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",
" seq_lens = batch[1]\n",
" labels = batch[2]\n",
"\n",
" logits = model(input_ids, seq_lens, labels=labels)[-1]\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",
" logits_all.extend(list(logits.detach().cpu().numpy()))\n",
"\n",
" tags = model.decode(logits, mask)\n",
" tags_all.extend([t for seq in tags for t in seq])\n",
"\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": 34,
"metadata": {
"id": "h0Dx-Opjd9iW"
},
"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\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_CRF(\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": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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},
"id": "unFGEN_2eIzo",
"outputId": "5fb7ea49-ac9f-48a7-936d-8809d196c70b"
},
"outputs": [
{
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" 0%| | 0/1756 [00:00<?, ?it/s]"
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{
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"Evaluation: 0%| | 0/1 [00:00<?, ?it/s]"
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},
{
"name": "stdout",
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"text": [
"[[42598 14 26 21 25 16 4 25 30]\n",
" [ 61 1654 9 48 0 62 2 5 1]\n",
" [ 25 7 1246 1 11 0 13 0 4]\n",
" [ 65 13 1 1086 19 97 0 58 2]\n",
" [ 76 1 8 4 531 9 54 2 66]\n",
" [ 27 4 0 18 3 1763 6 16 0]\n",
" [ 9 0 0 0 7 3 228 0 10]\n",
" [ 72 12 0 26 3 17 0 775 17]\n",
" [ 27 1 8 1 16 2 4 12 275]]\n",
"Validation F1: 0.8698924809029651, train loss: 0.10063888397788287\n"
]
},
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"Traceback (most recent call last):\n",
" File \"/Users/knf792/miniconda3/envs/nlp-course/lib/python3.10/multiprocessing/queues.py\", line 241, in _feed\n",
" close()\n",
" File \"/Users/knf792/miniconda3/envs/nlp-course/lib/python3.10/multiprocessing/connection.py\", line 182, in close\n",
" self._close()\n",
" File \"/Users/knf792/miniconda3/envs/nlp-course/lib/python3.10/multiprocessing/connection.py\", line 366, in _close\n",
" _close(self._handle)\n",
"OSError: [Errno 9] Bad file descriptor\n"
]
},
{
"name": "stdout",
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"Validation F1: 0.8821901555472853, train loss: 0.04108418301545575\n"
]
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"Validation F1: 0.9217032781476764, train loss: 0.0023582927215561266\n"
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"Validation F1: 0.9240073784348178, train loss: 0.0015345649171752004\n"
]
},
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 35,
"metadata": {},
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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=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": "code",
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"metadata": {
"colab": {
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"[[37831 61 11 156 78 49 14 67 56]\n",
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],
"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": "code",
"execution_count": 38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0pO3u3AwjVfj",
"outputId": "413cb2a6-7a66-4b56-8006-90943b52da05"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('SOCCER', 'O', 'O'), ('-', 'O', 'O'), ('JAPAN', 'B-LOC', 'B-LOC'), ('GET', 'O', 'O'), ('LUCKY', 'B-ORG', 'O'), ('WIN', 'O', 'O'), (',', 'O', 'O'), ('CHINA', 'B-LOC', 'B-PER'), ('IN', 'O', 'O'), ('SURPRISE', 'O', 'O'), ('DEFEAT', 'O', 'O'), ('.', 'O', 'O')]\n",
"[('AL-AIN', 'B-LOC', 'B-LOC'), (',', 'O', 'O'), ('United', 'B-LOC', 'B-LOC'), ('Arab', 'I-LOC', 'I-LOC'), ('Emirates', 'I-LOC', 'I-LOC'), ('1996-12-06', 'O', 'O')]\n"
]
}
],
"source": [
"model.eval()\n",
"examples = [0, 2]\n",
"for ex in examples:\n",
" samples = [b.to(device) for b in next(iter(test_dl))]\n",
"\n",
" # Get the emissions. These are basically p(y|x) for each token x,\n",
" # which will be input to the CRF a decoded with the help of p(y_t|y_{t-1})\n",
" (emissions,) = model(samples[0], samples[1])\n",
" mask = (samples[0] != 0)\n",
"\n",
" tags = model.decode(emissions, mask)\n",
"\n",
" print([(tok, datasets[\"train\"].features[f\"ner_tags\"].feature.names[tag], datasets[\"train\"].features[f\"ner_tags\"].feature.names[gold_tag]) \n",
" for tok,tag, gold_tag in zip(datasets['test'][ex]['tokens'], tags[ex], datasets['test'][ex]['ner_tags'])])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After training the model, we can inspect the CRF layer and check the learned transition matrix $V = p(y_t|y_{t-1})$. For example, we can see that the most probable transition from B-PER is I-PER, as expected."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"O: 0.04908164590597153\n",
"B-PER: 2.7868492907145992e-05\n",
"I-PER: 0.9053760766983032\n",
"B-ORG: 0.0009374959045089781\n",
"I-ORG: 0.00010273537918692455\n",
"B-LOC: 0.027795543894171715\n",
"I-LOC: 0.00016327288176398724\n",
"B-MISC: 0.016292331740260124\n",
"I-MISC: 0.00022300780983641744\n"
]
}
],
"source": [
"b_per_id = datasets[\"train\"].features[f\"ner_tags\"].feature.names.index(\"B-PER\")\n",
"transitions = model.model[\"CRF\"].transitions[b_per_id].detach().to(\"cpu\")\n",
"transitions = torch.softmax(transitions, 0).numpy()\n",
"for idx, tag in enumerate(datasets[\"train\"].features[f\"ner_tags\"].feature.names):\n",
" print(f\"{tag}: {transitions[idx]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question:** How would you implement a Transformer-CRF?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YATCbeeTDNQG"
},
"source": [
"# Beam Search"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sylZvVBm0lo_"
},
"source": [
" [(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",
"\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": [
"e69a0f48b8104bd29adfab7a1409d1d4",
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"7ec12acc502c4bafa871a289054204b2",
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"id": "iHvNb7nm6kLI",
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"outputs": [
{
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" 0%| | 0/220 [00:00<?, ?it/s]"
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"text/plain": [
"Evaluation: 0%| | 0/3250 [00:00<?, ?it/s]"
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{
"name": "stdout",
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"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",
" [ 285 12 25 65 323 10 13 5 13]\n",
" [ 627 35 10 91 23 951 46 35 19]\n",
" [ 92 4 5 12 26 22 79 1 16]\n",
" [ 535 16 8 24 9 34 6 246 44]\n",
" [ 154 5 5 22 24 18 12 14 92]]\n",
"Validation F1: 0.5148684803742811, train loss: 0.1254397713663903\n"
]
},
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"text/plain": [
"Evaluation: 0%| | 0/3250 [00:00<?, ?it/s]"
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},
"metadata": {},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[40938 372 251 212 345 316 34 181 110]\n",
" [ 353 1221 148 35 26 28 2 20 9]\n",
" [ 329 94 822 14 28 5 0 3 12]\n",
" [ 275 39 10 820 114 43 2 32 6]\n",
" [ 190 2 17 58 435 8 7 6 28]\n",
" [ 404 21 3 66 24 1264 22 26 7]\n",
" [ 84 3 5 3 32 23 97 0 10]\n",
" [ 342 15 3 35 21 60 2 414 30]\n",
" [ 141 4 3 9 30 12 15 13 119]]\n",
"Validation F1: 0.5992961020420663, train loss: 0.08672628991983154\n"
]
},
{
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},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[41156 413 324 116 301 233 76 99 41]\n",
" [ 341 1254 170 13 19 29 4 7 5]\n",
" [ 279 119 866 2 16 12 5 3 5]\n",
" [ 249 59 8 851 109 38 5 18 4]\n",
" [ 137 9 24 30 511 6 13 5 16]\n",
" [ 345 27 9 59 25 1314 26 29 3]\n",
" [ 51 3 8 8 22 27 126 1 11]\n",
" [ 350 22 4 39 10 27 0 459 11]\n",
" [ 132 3 5 11 30 8 12 28 117]]\n",
"Validation F1: 0.6389387782477018, train loss: 0.06806593586436727\n"
]
},
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},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[40798 449 320 148 528 183 57 120 156]\n",
" [ 225 1307 201 18 25 34 6 7 19]\n",
" [ 237 98 914 5 22 10 9 0 12]\n",
" [ 144 59 21 890 147 52 6 17 5]\n",
" [ 98 4 23 22 578 1 9 0 16]\n",
" [ 255 33 9 62 36 1372 25 21 24]\n",
" [ 36 1 5 4 38 14 139 2 18]\n",
" [ 266 33 8 66 27 30 1 450 41]\n",
" [ 83 5 10 10 68 1 6 12 151]]\n",
"Validation F1: 0.6429197452938862, train loss: 0.057959155670621175\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>"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
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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": {
"id": "R80wA-upBHDD"
},
"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"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 311
},
"id": "o1yYPFe5wKC5",
"outputId": "95db2ec2-2fdf-444b-9dad-42c2d7615094"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a8d16dfe21b74ea98e7853171b8c66a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Evaluation: 0%| | 0/3453 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[37092 237 111 313 218 97 32 133 90]\n",
" [ 188 1254 51 73 19 20 0 9 3]\n",
" [ 177 55 868 3 38 4 4 3 4]\n",
" [ 133 58 4 1264 77 69 2 53 1]\n",
" [ 93 4 12 33 639 7 30 2 15]\n",
" [ 140 42 5 107 24 1304 11 32 3]\n",
" [ 26 0 10 4 33 9 165 1 9]\n",
" [ 94 23 6 57 10 16 1 479 16]\n",
" [ 45 3 8 1 23 1 2 11 122]]\n"
]
},
{
"data": {
"text/plain": [
"0.727843916681434"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 311,
"referenced_widgets": [
"a3e56bc011584c638aba5ebb200cc409",
"2c62f6825ac549668b5adb970201f9ee",
"311130cce54541aeb64ed9ee04338e85",
"e194b1b75f4c40b88e7ecf6b7ce308f4",
"6ffb7f64d4924c73b468a160a730d785",
"6d84ccb32486488db4eb66ca7401b3c2",
"6aa949d213804e3ba5bfdfb1b8c89794",
"02dd4f78a3e74dbe8c6a3012fe1225d5",
"7926b0ae95a643ad90c0341340cf2b43",
"eff6ea1ca2d1415683784d3ce95a9f07",
"a8d985e224b64b6baa282f4d00a00a74"
]
},
"id": "5jBpWDyExq1q",
"outputId": "068ef3dc-d1db-4fec-8135-726334a8dca4"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a3e56bc011584c638aba5ebb200cc409",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Evaluation: 0%| | 0/3453 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[37128 232 121 311 209 93 32 128 69]\n",
" [ 185 1270 49 68 14 18 0 10 3]\n",
" [ 163 60 887 4 32 2 1 3 4]\n",
" [ 135 64 5 1275 66 70 2 43 1]\n",
" [ 86 6 15 30 645 8 30 2 13]\n",
" [ 126 41 7 105 26 1314 16 31 2]\n",
" [ 28 0 10 4 36 7 167 0 5]\n",
" [ 92 26 6 61 10 16 2 476 13]\n",
" [ 48 2 8 1 22 1 2 12 120]]\n"
]
},
{
"data": {
"text/plain": [
"0.7355817928230471"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluate(model, test_dl, beam_size=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qT6slqcmhu-4"
},
"source": [
"# Learning rate schedules"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ndey9-pwhCqW"
},
"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": {
"id": "3Kqpap96inI7"
},
"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": "code",
"execution_count": null,
"metadata": {
"id": "k7GltqL0NYsK"
},
"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\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_CRF(\n",
" pretrained_embeddings=torch.FloatTensor(pretrained_embeddings), \n",
" lstm_dim=lstm_dim, \n",
" dropout_prob=dropout_prob, \n",
" n_classes=len(label_map)\n",
" ).to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": {
"id": "JclJEGDxiw0S"
},
"source": [
"Above we have used the `CyclicLR` scheduler. The cyclic learning rate schedule in general looks like this:\n",
"\n",
" [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": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "le4bSXr6O4w1",
"outputId": "339c5893-2ac0-44c2-d1b0-173a91714386"
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f75e401cd50>]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(losses)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "52VPU3yWDLh9",
"outputId": "18366141-3468-428d-9f67-22c4270cd737"
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f75e54d1790>]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
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}
],
"source": [
"plt.plot(learning_rates)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mDcsAYEKFJB3"
},
"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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