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"cells": [
{
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"metadata": {
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},
"source": [
"Link to the notebook: https://drive.google.com/file/d/1pKE2nMcLo4kpp_maZly6XDP4aUFnjDlI/view\n",
"Copy the notebook to your GDrive to edit. (to update)"
]
},
{
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"source": [
"# Download prerequisite packages\n",
"\n",
"First we will install extra packages which aren't included in the default colab environment. In this case, we are installing `gensim` for GloVe embeddings, `bpemb` for word-piece embeddings and tokenization, and some extra packages for tokenization."
]
},
{
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"metadata": {
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},
"outputId": "79a98d67-ef1d-4686-c1fe-5f912a148896"
},
"source": [
"!pip install bpemb\n",
"!pip install gensim\n",
"!python -m spacy download en_core_web_sm"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: bpemb in /usr/local/lib/python3.7/dist-packages (0.3.3)\n",
"Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from bpemb) (0.1.97)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from bpemb) (1.21.6)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from bpemb) (4.64.1)\n",
"Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (from bpemb) (3.6.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from bpemb) (2.23.0)\n",
"Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim->bpemb) (5.2.1)\n",
"Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim->bpemb) (1.15.0)\n",
"Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim->bpemb) (1.7.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->bpemb) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->bpemb) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->bpemb) (2022.6.15)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->bpemb) (3.0.4)\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (3.6.0)\n",
"Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.15.0)\n",
"Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.7.3)\n",
"Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.21.6)\n",
"Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim) (5.2.1)\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting en-core-web-sm==3.4.0\n",
" Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.0/en_core_web_sm-3.4.0-py3-none-any.whl (12.8 MB)\n",
"\u001b[K |████████████████████████████████| 12.8 MB 7.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: spacy<3.5.0,>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from en-core-web-sm==3.4.0) (3.4.1)\n",
"Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (8.1.0)\n",
"Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (1.21.6)\n",
"Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (0.10.1)\n",
"Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.0.8)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (21.3)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.11.3)\n",
"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (1.0.8)\n",
"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.0.6)\n",
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"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the package via spacy.load('en_core_web_sm')\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"import nltk\n",
"nltk.download('stopwords')"
],
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Bt0lqVTt7Sn0",
"outputId": "f8edf44c-a256-49ad-ed83-f5bf990f6858"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "lNjcXpu4_9CP"
},
"source": [
"Here we are just using some magic commands to make sure changes to external packages are automatically loaded and plots are displayed in the notebook."
]
},
{
"cell_type": "code",
"metadata": {
"id": "vKjNPo57FTJ8"
},
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3ut_Y8P_BLw8"
},
"source": [
"# Ensuring reproducible results\n",
"\n",
"Before we do anything else, we need to make sure that we seed all of the random number generators and set some flags to ensure that our results are reproducible across runs i.e. that we get the same result every time we run our code."
]
},
{
"cell_type": "code",
"metadata": {
"id": "y7aI_mECQqhS"
},
"source": [
"import torch\n",
"import random\n",
"import numpy as np"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JkmVtpi_MxEc"
},
"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)"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jQMckQKCTtpO"
},
"source": [
"enforce_reproducibility()"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "O9rrFIfPg3AS"
},
"source": [
"# Representations in NLP\n",
"\n",
"- We need some way to represent our text numerically when working with ML systems\n",
"- Transform our inuput text into a vector or a sequence of vectors depending on the method used \n",
"- How to encode individual tokens?\n",
"- One way is to have a vocabulary of all possible tokens, assign each token a number, and encode them as a one-hot vector\n",
"\n",
"- Couple of problems\n",
" - Inefficient: defines one feature for every word in the vocabulary\n",
" - All words are orthogonal to each other, so what model learns about one word doesn't apply to similar words\n",
"- Solution: continuous word representations\n",
" - Define each token in your vocab to be a $d$-dimensional vector\n",
" - Train these vectors such that similar words have closer vectors (e.g. as measured by cosine distance)\n",
" - Methods which do this are based on the \"distributional hypothesis\": similar words appear in similar contexts\n",
" - E.g. word2vec trains word vectors to be able to predict the surrounding words (or vice-versa)\n",
" - word2vec vectors: https://projector.tensorflow.org/\n",
"- Here we'll look at how to load/use two types of word embeddings: GloVe and byte-pair encoding (BPE) embeddings\n",
" - GloVe is trained on word tokens using global word co-occurence statistics ([description here](https://nlp.stanford.edu/projects/glove/#:~:text=The%20training%20objective%20of%20GloVe,'%20probability%20of%20co%2Doccurrence.&text=For%20this%20reason%2C%20the%20resulting,examined%20in%20the%20word2vec%20package.))\n",
" - BPE works by iteratively building a vocabulary of size N via merging the most frequent character n-grams from a large text corpus (e.g. Wikipedia). The resulting tokens aren't necessarily words, but are _word pieces_. As a result, you no longer have a problem where a given token doesn't appear in your vocabulary -- you can always deconstruct a word into one or more word pieces. The embeddings for these word pieces are trained using GloVe in the `bpemb` package. Read more [here](https://github.com/bheinzerling/bpemb) "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ta5eJlXDi0vx"
},
"source": [
"## GloVe embeddings\n",
"\n",
"- For this we can use `gensim` which has a large variety of pre-trained word embeddings\n",
"- Check out their docs https://radimrehurek.com/gensim/auto_examples/index.html#documentation\n",
"- We first load 100-d embeddings trained on Wikipedia and the Gigaword corpus\n",
"- We can then examine what are the most similar words to some given words"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jcl02lErhAQ0",
"outputId": "a8f0d6c6-8ecc-43ff-f077-8bb03603b4cb"
},
"source": [
"import gensim.downloader\n",
"\n",
"print(list(gensim.downloader.info()['models'].keys()))"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['fasttext-wiki-news-subwords-300', 'conceptnet-numberbatch-17-06-300', 'word2vec-ruscorpora-300', 'word2vec-google-news-300', 'glove-wiki-gigaword-50', 'glove-wiki-gigaword-100', 'glove-wiki-gigaword-200', 'glove-wiki-gigaword-300', 'glove-twitter-25', 'glove-twitter-50', 'glove-twitter-100', 'glove-twitter-200', '__testing_word2vec-matrix-synopsis']\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1gaVG9CDhspf"
},
"source": [
"# Load the vectors\n",
"\n",
"glove_vectors = gensim.downloader.load('glove-wiki-gigaword-100')"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CYtrchThipk6",
"outputId": "07d6810d-19a8-4039-88cc-9539b8124fff"
},
"source": [
"# Look at some of the most similar words\n",
"glove_vectors.most_similar('great')"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[('greatest', 0.7882647514343262),\n",
" ('good', 0.759279727935791),\n",
" ('little', 0.7585746049880981),\n",
" ('much', 0.7477047443389893),\n",
" ('well', 0.7401014566421509),\n",
" ('big', 0.7318841218948364),\n",
" ('kind', 0.7308766841888428),\n",
" ('important', 0.7286974191665649),\n",
" ('there', 0.7225556373596191),\n",
" ('way', 0.7116187214851379)]"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "M-MIkvTUqgyD",
"outputId": "8e64a95c-84bd-47a3-fe92-1a7cbd1820fe"
},
"source": [
"# Get vector representation of word\n",
"glove_vectors['great']"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([-0.013786 , 0.38216 , 0.53236 , 0.15261 , -0.29694 ,\n",
" -0.20558 , -0.41846 , -0.58437 , -0.77355 , -0.87866 ,\n",
" -0.37858 , -0.18516 , -0.128 , -0.20584 , -0.22925 ,\n",
" -0.42599 , 0.3725 , 0.26077 , -1.0702 , 0.62916 ,\n",
" -0.091469 , 0.70348 , -0.4973 , -0.77691 , 0.66045 ,\n",
" 0.09465 , -0.44893 , 0.018917 , 0.33146 , -0.35022 ,\n",
" -0.35789 , 0.030313 , 0.22253 , -0.23236 , -0.19719 ,\n",
" -0.0053125, -0.25848 , 0.58081 , -0.10705 , -0.17845 ,\n",
" -0.16206 , 0.087086 , 0.63029 , -0.76649 , 0.51619 ,\n",
" 0.14073 , 1.019 , -0.43136 , 0.46138 , -0.43585 ,\n",
" -0.47568 , 0.19226 , 0.36065 , 0.78987 , 0.088945 ,\n",
" -2.7814 , -0.15366 , 0.01015 , 1.1798 , 0.15168 ,\n",
" -0.050112 , 1.2626 , -0.77527 , 0.36031 , 0.95761 ,\n",
" -0.11385 , 0.28035 , -0.02591 , 0.31246 , -0.15424 ,\n",
" 0.3778 , -0.13599 , 0.2946 , -0.31579 , 0.42943 ,\n",
" 0.086969 , 0.019169 , -0.27242 , -0.31696 , 0.37327 ,\n",
" 0.61997 , 0.13889 , 0.17188 , 0.30363 , -1.2776 ,\n",
" 0.044423 , -0.52736 , -0.88536 , -0.19428 , -0.61947 ,\n",
" -0.10146 , -0.26301 , -0.061707 , 0.36627 , -0.95223 ,\n",
" -0.39346 , -0.69183 , -1.0426 , 0.28855 , 0.63056 ],\n",
" dtype=float32)"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HTsS5hx5i739",
"outputId": "d93c18e7-0aa6-4de6-a004-b9b424b398e1"
},
"source": [
"glove_vectors.most_similar('bad')"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[('worse', 0.7929712533950806),\n",
" ('good', 0.7702798247337341),\n",
" ('things', 0.7653602361679077),\n",
" ('too', 0.7630148530006409),\n",
" ('thing', 0.7609667778015137),\n",
" ('lot', 0.7443647384643555),\n",
" ('kind', 0.740868091583252),\n",
" ('because', 0.739879846572876),\n",
" ('really', 0.7376541495323181),\n",
" (\"n't\", 0.7336541414260864)]"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PLs-HGKPi41P"
},
"source": [
"## BPEmb embeddings\n",
"\n",
"- Here we use `bpemb`which has pretrained BPE tokenizers/embeddings for 275 languages\n",
"- https://github.com/bheinzerling/bpemb\n",
"- First we load the English model with 25,000 word pieces and 100-dimensions\n",
"- The package has some similar functionality built in to `gensim`, for example observing the top similar words to a given word"
]
},
{
"cell_type": "code",
"metadata": {
"id": "byruqtK7jIms"
},
"source": [
"from bpemb import BPEmb\n",
"\n",
"# Load english model with 25k word-pieces\n",
"bpemb_en = BPEmb(lang='en', dim=100, vs=25000)"
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "S3g2K36pjLVq",
"outputId": "5dbb03af-3ce2-46e4-d586-1a2a0ba23832"
},
"source": [
"bpemb_en.most_similar('great')"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[('little', 0.5838008522987366),\n",
" ('▁great', 0.5713019371032715),\n",
" ('grand', 0.4832311272621155),\n",
" ('the', 0.47381392121315),\n",
" ('▁glorious', 0.4674184322357178),\n",
" ('bear', 0.4492991268634796),\n",
" ('wh', 0.4481717348098755),\n",
" ('mother', 0.44679754972457886),\n",
" ('▁splend', 0.44266074895858765),\n",
" ('southern', 0.427338182926178)]"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "s4yyZCTHqtcy",
"outputId": "da5af2fe-e34e-42ac-bf04-f8520796746c"
},
"source": [
"bpemb_en['great']"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 0.188451, -0.271453, -0.28907 , 0.061539, -0.00119 , 0.023874,\n",
" -0.067886, -0.331542, 0.216387, 0.357444, -0.37975 , 0.568398,\n",
" 0.094106, 0.131808, -0.225529, -0.0577 , -0.471579, 0.270219,\n",
" 0.500174, -0.351869, 0.249958, -0.083147, -0.196093, 0.536826,\n",
" -0.472552, -0.290486, 0.044731, 0.291949, -0.02748 , 0.833167,\n",
" -0.702959, 0.102262, -0.192826, 0.323853, -0.222146, -0.471987,\n",
" -0.789 , -0.384075, 0.231821, 0.479966, -0.23674 , -0.233447,\n",
" -0.095288, 0.994725, 0.38566 , 0.539499, -0.117075, -0.424706,\n",
" -0.35617 , -0.073661, 0.185955, -0.329411, -0.336546, -0.883611,\n",
" 0.450815, -0.087026, 0.547418, -0.177824, -0.50549 , 0.423664,\n",
" 0.15182 , 0.289324, 0.546515, -0.963773, 0.041765, 0.526582,\n",
" -0.336767, 0.128516, 0.307212, 0.049208, -0.314852, 0.343516,\n",
" 0.09675 , 0.033849, -0.260953, 0.340189, -0.114852, 0.128735,\n",
" 0.403689, -0.154596, 0.303449, 0.855851, 0.089396, 0.229577,\n",
" 0.022571, -0.194975, 0.080064, 0.273517, 0.394372, -0.196653,\n",
" 0.220832, -0.052137, -0.274584, 0.376419, 0.151644, -0.01065 ,\n",
" 0.195909, -0.456158, 0.042357, 0.058726], dtype=float32)"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-XIFDVmrjbDW",
"outputId": "b98b7d04-e400-408d-d80e-39304dbac471"
},
"source": [
"bpemb_en.most_similar('bad')"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[('▁bad', 0.7139850854873657),\n",
" ('bir', 0.5771083831787109),\n",
" ('her', 0.5590745806694031),\n",
" ('hol', 0.5386293530464172),\n",
" ('mad', 0.5319529175758362),\n",
" ('wal', 0.5289992690086365),\n",
" ('wolf', 0.5198653340339661),\n",
" ('hal', 0.5146304368972778),\n",
" ('▁luck', 0.5110129117965698),\n",
" ('ün', 0.506809651851654)]"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p13nASL6mnsm"
},
"source": [
"# Sentiment classification of movie reviews\n",
"\n",
"We'll see how to train a basic logistic regression classifier for sentiment analysis. For this, we're using the [IMDB movie reviews dataset](https://www.kaggle.com/datasets/ducanger/imdb-dataset). "
]
},
{
"cell_type": "code",
"metadata": {
"id": "z8lzv8D7oz6V"
},
"source": [
"import spacy\n",
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.model_selection import RandomizedSearchCV\n",
"from sklearn.metrics import classification_report"
],
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": "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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 38
},
"id": "f0z6eqUMmq0l",
"outputId": "99653418-61a5-48e4-94d5-fd72351dcafa"
},
"source": [
"# Upload the data from local computer \n",
"from google.colab import files\n",
"\n",
"uploaded = files.upload()\n",
"\n",
"for fn in uploaded.keys():\n",
" print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
" name=fn, length=len(uploaded[fn])))"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-535ebfab-9cfc-4bfa-92c1-38fb1806afea\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-535ebfab-9cfc-4bfa-92c1-38fb1806afea\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# or you can load from google drive if you upload the files there (faster in my experience)\n",
"from google.colab import drive\n",
"drive.mount('/content/drive') # this will trigger permission prompts\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gTuuSYH88HFz",
"outputId": "a1d6c991-6545-44ea-ff74-8b66cf870011"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "cfxyV0umowmM",
"outputId": "334e2362-9762-4391-849d-0c6d31ac9d97"
},
"source": [
"train_data = pd.read_csv('/content/drive/My Drive/nlp_labs/lab_2/imdb/train.csv', usecols=[1,2]).fillna('')\n",
"valid_data = pd.read_csv('/content/drive/My Drive/nlp_labs/lab_2/imdb/val.csv', usecols=[1,2]).fillna('')\n",
"test_data = pd.read_csv('/content/drive/My Drive/nlp_labs/lab_2/imdb/test.csv', usecols=[1,2]).fillna('')\n",
"valid_data.head() # in the sentiment column, 1 is positive, 0 is negative"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" review sentiment\n",
"0 A genuinely odd, surreal jumble of visual idea... 0\n",
"1 \"The Snow Queen\" is based on the famous and ve... 0\n",
"2 The quintessential Georgian film of Georgi Dan... 1\n",
"3 I'm a huge comedy show fan. Racial humor is al... 0\n",
"4 Pretty good film from Preminger; labyrinthine ... 1"
],
"text/html": [
"\n",
" <div id=\"df-e574b82e-91e4-4300-a213-ab1db97876ad\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>review</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A genuinely odd, surreal jumble of visual idea...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>\"The Snow Queen\" is based on the famous and ve...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>The quintessential Georgian film of Georgi Dan...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>I'm a huge comedy show fan. Racial humor is al...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Pretty good film from Preminger; labyrinthine ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e574b82e-91e4-4300-a213-ab1db97876ad')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-e574b82e-91e4-4300-a213-ab1db97876ad button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-e574b82e-91e4-4300-a213-ab1db97876ad');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2joLhlgU6CPe",
"outputId": "b05e66ff-793d-4667-f0bd-f07df5e4d767"
},
"source": [
"train_data.values[0]"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([\"Having avoided seeing the movie in the cinema, but buying the DVD for my wife for Xmas, I had to watch it. I did not expect much, which usually means I get more than I bargained for. But 'Mamma Mia' - utter, utter cr**. I like ABBA, I like the songs, I have the old LPs. But this film is just terrible. The stage show looks like a bit of a musical, but this races along with songs hurriedly following one another, no characterisation, the dance numbers (which were heavily choreographed according to the extras on the DVD) are just thrown away with only half the bodies ever on screen, the dance chorus of north Europeans appear on a small Greek island at will, while the set and set up of numbers would have disgraced Cliff Richard's musicals in the sixties!Meryl (see me I'm acting)Streep can't even make her usual mugging effective in an over-the-top musical! Her grand piece - 'The Winner Takes It All' - is Meryl at the Met! Note to director - it should have been shot in stillness with the camera gradually showing distance growing between Streep and Brosnan! Some of the singing is awful karaoke on amateur night. The camera cannot stop moving like bad MTV. One can never settle down and just enjoy the music, enthusiasm and characters. But what is even worse is how this botched piece of excre**** has become the highest grossing film in the UK and the best selling DVD to boot? Blair, Campbell and New Labour really have reduced the UK to zombies - critical faculties anyone???\",\n",
" 0], dtype=object)"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eDSgEyswPPG2"
},
"source": [
"This defines some functions for vectorizing our text. For GloVe and BPE embeddings, this involves:\n",
"\n",
"1. Tokenizing the text\n",
"2. Looking up the embedding for each token\n",
"3. Pooling the representations to obtain a single vector for the whole document\n",
"\n",
"There are many options for pooling e.g. max-pooling, average-pooling, summation, etc."
]
},
{
"cell_type": "code",
"metadata": {
"id": "p5REmUCZpNJU"
},
"source": [
"from tqdm import tqdm\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"stopwords = set(stopwords.words('english'))\n",
"\n",
"\n",
"def get_unigram_features(dataset, vectorizer):\n",
" X = vectorizer.transform(dataset[:,0])\n",
" y = list(dataset[:,1])\n",
" return X,y\n",
"\n",
"def get_bpemb_features(dataset, bpemb):\n",
" # With bpemb we can tokenize and embed an entire document using .embed(x)\n",
" X = [bpemb.embed(x).mean(0) for x in tqdm(dataset[:,0])]\n",
" y = list(dataset[:,1])\n",
" return X,y\n",
" \n",
"def get_glove_features(dataset, glove, nlp):\n",
" X = []\n",
" for x in tqdm(dataset[:,0]):\n",
" # For glove embeddings, we first tokenize the sentence using spacy, lower-case and remove stopwords, and get the available word vectors\n",
" vecs = np.vstack([glove[t.text.lower()] if t.text.lower() in glove else np.zeros(100) for t in nlp(x) if t.text.lower() not in stopwords])\n",
" X.append(vecs.mean(0))\n",
" y = list(dataset[:,1])\n",
" return np.stack(X),y"
],
"execution_count": 21,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "GjI-auvMP0ES"
},
"source": [
"This defines how to run a basic logistic regression classifier. Commented out are some code for if you want to run a hyperparameter search."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xjw2KFxnp9zW"
},
"source": [
"def run_classifier(X_train, y_train, X_test, y_test):\n",
" # Define hyperparams for search\n",
" # C = np.logspace(-6, 2, 50)\n",
" # warm_start = [False, True]\n",
" # class_weight = ['balanced', None]\n",
"\n",
" # hp = {\"C\": C, \"warm_start\": warm_start, 'class_weight': class_weight}\n",
"\n",
" classifier = LogisticRegression(penalty='l2', max_iter=1000)\n",
" # classifier_random = RandomizedSearchCV(\n",
" # estimator=classifier,\n",
" # param_distributions=hp,\n",
" # n_iter=100,\n",
" # cv=5,\n",
" # verbose=2,\n",
" # random_state=1000,\n",
" # n_jobs=-1,\n",
" # scoring='f1'\n",
" # )\n",
"\n",
" # classifier_random.fit(X_train, y_train)\n",
" # print(classifier_random.best_params_)\n",
" # print(classifier_random.best_score_)\n",
" # model = classifier_random.best_estimator_\n",
" classifier.fit(X_train,y_train)\n",
" preds = classifier.predict(X_test)\n",
" print(classification_report(y_test, preds))"
],
"execution_count": 22,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "q8Vu2FpFP9UI"
},
"source": [
"First we'll run GloVe"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-oGRK6sUuYON",
"outputId": "dbce17dc-c9cf-49e6-8c65-efd30895399e"
},
"source": [
"#only train and test on part of the dataset as an example\n",
"nlp = spacy.load('en_core_web_sm')\n",
"X_train,y_train = get_glove_features(train_data.values[:5000], glove_vectors, nlp)\n",
"X_test,y_test = get_glove_features(test_data.values[:1000], glove_vectors, nlp)\n",
"run_classifier(X_train, y_train, X_test, y_test)"
],
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 5000/5000 [03:01<00:00, 27.55it/s]\n",
"100%|██████████| 1000/1000 [00:35<00:00, 28.08it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.82 0.79 0.80 524\n",
" 1 0.78 0.80 0.79 476\n",
"\n",
" accuracy 0.80 1000\n",
" macro avg 0.80 0.80 0.80 1000\n",
"weighted avg 0.80 0.80 0.80 1000\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3a4105HNQAGz"
},
"source": [
"Next BPE"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "srVQR_-6yDQJ",
"outputId": "48121b2e-0ab8-46da-aa18-da3454c9abf3"
},
"source": [
"X_train,y_train = get_bpemb_features(train_data.values[:5000], bpemb_en)\n",
"X_test,y_test = get_bpemb_features(test_data.values[:1000], bpemb_en)\n",
"run_classifier(X_train, y_train, X_test, y_test)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 5000/5000 [00:04<00:00, 1157.32it/s]\n",
"100%|██████████| 1000/1000 [00:00<00:00, 1143.96it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.78 0.79 0.78 524\n",
" 1 0.76 0.75 0.76 476\n",
"\n",
" accuracy 0.77 1000\n",
" macro avg 0.77 0.77 0.77 1000\n",
"weighted avg 0.77 0.77 0.77 1000\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jn8IBZO0FI1k"
},
"source": [
"Just as a note, you can actually get much better performance using simple word counts -- why do you think this is?"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PDMW99tCVW3i",
"outputId": "e7f1c29b-0535-4aa6-be4b-92be564602db"
},
"source": [
"# Run on unigram features\n",
"vectorizer = CountVectorizer()\n",
"vectorizer.fit(train_data.values[:,0])\n",
"X_train,y_train = get_unigram_features(train_data.values[:5000], vectorizer)\n",
"X_test,y_test = get_unigram_features(test_data.values[:1000], vectorizer)\n",
"run_classifier(X_train, y_train, X_test, y_test)"
],
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.89 0.84 0.87 524\n",
" 1 0.83 0.89 0.86 476\n",
"\n",
" accuracy 0.86 1000\n",
" macro avg 0.86 0.86 0.86 1000\n",
"weighted avg 0.87 0.86 0.86 1000\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ouOJTiDOFSun"
},
"source": [
"# Sentiment analysis using a Bi-LSTM"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ciGgJEDwPaP4"
},
"source": [
"# Reading data into a model\n",
"\n",
"A simple and common way that data is read in PyTorch is to use the two following classes: `torch.utils.data.Dataset` and `torch.utils.data.DataLoader`. \n",
"\n",
"The `Dataset` class can be extended to read in and store the data you are using for your experiment. The only requirements are to implement the `__len__` and `__getitem__` methods. `__len__` simply returns the size of your dataset and `__getitem__` takes an index and returns that sample from your dataset, processed in whatever way is necessary to be input to your model.\n",
"\n",
"The `DataLoader` class determines how to iterate through your `Dataset`, including how to shuffle and batch your data.\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ym9eaRzpBFfZ"
},
"source": [
"from torch.utils.data import Dataset, DataLoader\n",
"from typing import List, Tuple"
],
"execution_count": 26,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "uspFf7yZFdyO"
},
"source": [
"This is a utility function which, given a list of text samples and a tokenizer, will tokenize the text and return the IDs of the tokens in the vocabulary. The tokenizer will split the text into individual word-pieces and convert these to IDs in the tokenizer vocabulary. In addition, it will return the length of each sequence, which is needed when inputting padded data into an RNN in PyTorch."
]
},
{
"cell_type": "code",
"metadata": {
"id": "z8gwnwv9hFuz"
},
"source": [
"def text_to_batch_bilstm(text: List, tokenizer, max_len=512) -> Tuple[List, List]:\n",
" \"\"\"\n",
" Creates a tokenized batch for input to a bilstm model\n",
" :param text: A list of sentences to tokenize\n",
" :param tokenizer: A tokenization function to use (i.e. fasttext)\n",
" :return: Tokenized text as well as the length of the input sequence\n",
" \"\"\"\n",
" # Some light preprocessing\n",
" input_ids = [tokenizer.encode_ids_with_eos(t)[:max_len] for t in text]\n",
"\n",
" return input_ids, [len(ids) for ids in input_ids]"
],
"execution_count": 27,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4FvRoD7rGC4K"
},
"source": [
"This is another utility function which defines how to combine data into a batch. Importantly, it determines the max length of a sequence in the batch and pads all of the samples with a `[PAD]` ID to this length."
]
},
{
"cell_type": "code",
"metadata": {
"id": "LJfzyoT_FJ_V"
},
"source": [
"def collate_batch_bilstm(input_data: Tuple) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n",
" \"\"\"\n",
" Combines multiple data samples into a single batch\n",
" :param input_data: The combined input_ids, seq_lens, and labels for the batch\n",
" :return: A tuple of tensors (input_ids, seq_lens, labels)\n",
" \"\"\"\n",
" input_ids = [i[0][0] for i in input_data]\n",
" seq_lens = [i[1][0] for i in input_data]\n",
" labels = [i[2] for i in input_data]\n",
"\n",
" max_length = max([len(i) for i in input_ids])\n",
"\n",
" # Pad all of the input samples to the max length (25000 is the ID of the [PAD] token)\n",
" input_ids = [(i + [25000] * (max_length - len(i))) for i in input_ids]\n",
"\n",
" # Make sure each sample is max_length long\n",
" assert (all(len(i) == max_length for i in input_ids))\n",
" return torch.tensor(input_ids), torch.tensor(seq_lens), torch.tensor(labels)"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "0vo7eBpGGpyT"
},
"source": [
"This is the class which reads the dataset and processes indivudal samples. It is initialized with a pandas `DataFrame` and a tokenizer."
]
},
{
"cell_type": "code",
"metadata": {
"id": "sQ7d_SW1FUkq"
},
"source": [
"# This will load the dataset and process it lazily in the __getitem__ function\n",
"class ClassificationDatasetReader(Dataset):\n",
" def __init__(self, df, tokenizer):\n",
" self.df = df\n",
" self.tokenizer = tokenizer\n",
"\n",
" def __len__(self):\n",
" return len(self.df)\n",
"\n",
" def __getitem__(self, idx):\n",
" row = self.df.values[idx]\n",
" # Calls the text_to_batch function\n",
" input_ids,seq_lens = text_to_batch_bilstm([row[0]], self.tokenizer)\n",
" label = row[1]\n",
" return input_ids, seq_lens, label"
],
"execution_count": 29,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7NE1DwDCHLpu"
},
"source": [
"Here we can test whether or not our implementation is correct. We first load english `bpemb` embeddings, which will be used to initialize the word embedding layer of our model, and the tokenizer which will be used in the dataset reader."
]
},
{
"cell_type": "code",
"metadata": {
"id": "wbmE_EQpPCEY"
},
"source": [
"# Extract the embeddings and add a randomly initialized embedding for our extra [PAD] token\n",
"pretrained_embeddings = np.concatenate([bpemb_en.emb.vectors, np.zeros(shape=(1,100))], axis=0)\n",
"# Extract the vocab and add an extra [PAD] token\n",
"vocabulary = bpemb_en.emb.index2word + ['[PAD]']"
],
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "U6beVKUCnJ2A",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "960ba9f6-ab74-4186-97ef-8200a1f77de8"
},
"source": [
"reader = ClassificationDatasetReader(valid_data, bpemb_en)\n",
"reader[0]"
],
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F0cjbDzfJ3pE"
},
"source": [
"# Creating the model\n",
"\n",
"Next we will create a BiLSTM model with BPE word-piece embeddings. In this case we will extend the PyTorch class `torch.nn.Module`. To create your own module, you need only define your model architecture in the `__init__` function, and define how tensors are processed by your model in the `__forward__` function."
]
},
{
"cell_type": "code",
"metadata": {
"id": "MBX9nXd8DTBA"
},
"source": [
"from torch import nn\n",
"\n",
"# Define a default lstm_dim\n",
"lstm_dim = 100"
],
"execution_count": 32,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qRuI--wZOsCY"
},
"source": [
"\n",
"# Define the model\n",
"class BiLSTMNetwork(nn.Module):\n",
" \"\"\"\n",
" Basic BiLSTM 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(BiLSTMNetwork, 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",
" 1,\n",
" batch_first=True,\n",
" dropout=dropout_prob,\n",
" bidirectional=True),\n",
" 'cls': nn.Linear(2*lstm_dim, n_classes)\n",
" })\n",
" self.n_classes = n_classes\n",
" self.dropout = nn.Dropout(p=dropout_prob)\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['cls'].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",
" # Max pool along the last dimension\n",
" ff_in = self.dropout(torch.max(lstm_out, 1)[0])\n",
" # Some magic to get the last output of the BiLSTM for classification (b x 2*lstm_dim)\n",
" #ff_in = lstm_out.gather(1, input_lens.view(-1,1,1).expand(lstm_out.size(0), 1, lstm_out.size(2)) - 1).squeeze()\n",
"\n",
" # Get logits (b x n_classes)\n",
" logits = self.model['cls'](ff_in).view(-1, self.n_classes)\n",
" outputs = (logits,)\n",
" if labels is not None:\n",
" # Xentropy loss\n",
" loss_fn = nn.CrossEntropyLoss()\n",
" loss = loss_fn(logits, labels)\n",
" outputs = (loss,) + outputs\n",
"\n",
" return outputs\n",
"\n"
],
"execution_count": 33,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "9d-X3p7ENzoV"
},
"source": [
"## Choosing the device to run on\n",
"\n",
"PyTorch tensors exist on a particular device: in order to use a GPU for example, the tensors in your network need to be moved to a GPU device. The following allows us to check if a GPU device is available and get a reference to that device which we can use to place tensors on it."
]
},
{
"cell_type": "code",
"metadata": {
"id": "q-l7gpJFNyfe"
},
"source": [
"device = torch.device(\"cpu\")\n",
"if torch.cuda.is_available():\n",
" device = torch.device(\"cuda\")"
],
"execution_count": 34,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bk5Q2-JdNqGN"
},
"source": [
"Here we actually instantiate the model and move it to the GPU."
]
},
{
"cell_type": "code",
"metadata": {
"id": "2gLT6MUrO5xw",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "52e20535-d286-4979-9cfa-cb689ea79e87"
},
"source": [
"# Create the model\n",
"model = BiLSTMNetwork(\n",
" pretrained_embeddings=torch.FloatTensor(pretrained_embeddings), \n",
" lstm_dim=lstm_dim, \n",
" dropout_prob=0.1, \n",
" n_classes=2\n",
" ).to(device)\n"
],
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:65: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1\n",
" \"num_layers={}\".format(dropout, num_layers))\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nsDib4zuPkOa"
},
"source": [
"# Training and evaluation\n",
"\n",
"Here we define a metric which we are trying to improve -- in this case the accuracy of the model with respect to the label -- and functions to evaluate and train the model."
]
},
{
"cell_type": "code",
"metadata": {
"id": "e43ArMISKwzh"
},
"source": [
"def accuracy(logits, labels):\n",
" logits = np.asarray(logits).reshape(-1, len(logits[0]))\n",
" labels = np.asarray(labels).reshape(-1)\n",
" return np.sum(np.argmax(logits, axis=-1) == labels).astype(np.float32) / float(labels.shape[0])"
],
"execution_count": 36,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "XHkYIaldX7bK"
},
"source": [
"A useful utility which gives us a progress bar"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ovjgriwYQ1v2"
},
"source": [
"from tqdm import tqdm_notebook as tqdm"
],
"execution_count": 37,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fDdHYcJ-QRn9"
},
"source": [
"This is a utility function which will take a model and a validation dataloader and return the current accuracy of the model against that dataset. We can use this to know when to save the model and to perform early stopping if desired."
]
},
{
"cell_type": "code",
"metadata": {
"id": "z2r3DzpSOq_l"
},
"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",
"\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)\n",
" labels_all.extend(list(labels.detach().cpu().numpy()))\n",
" logits_all.extend(list(logits.detach().cpu().numpy()))\n",
" acc = accuracy(logits_all, labels_all)\n",
"\n",
" return acc,labels_all,logits_all"
],
"execution_count": 38,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ykX39dqiQ3qI"
},
"source": [
"Here we define the main training loop."
]
},
{
"cell_type": "code",
"metadata": {
"id": "7aKieRBZkghS"
},
"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",
" patience: int = 10\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_acc = 0.0\n",
" pcounter = 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",
" #gc.collect()\n",
"\n",
" # Perform inline evaluation at the end of the epoch\n",
" acc,_,_ = evaluate(model, valid_dl)\n",
" print(f'Validation accuracy: {acc}, train loss: {sum(loss_epoch) / len(loss_epoch)}')\n",
"\n",
" # Keep track of the best model based on the accuracy\n",
" if acc > best_acc:\n",
" torch.save(model.state_dict(), 'best_model')\n",
" best_acc = acc\n",
" pcounter = 0\n",
" else:\n",
" pcounter += 1\n",
" if pcounter == patience:\n",
" break\n",
" #gc.collect()\n",
"\n",
" model.load_state_dict(torch.load('best_model'))\n",
" return model, losses"
],
"execution_count": 39,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ANhsatD4S4C5"
},
"source": [
"Now that we have the basic training and evaluation loops defined, we can create the datasets and optimizer and run it!"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BT85gSAXKqYc"
},
"source": [
"from torch.optim import Adam"
],
"execution_count": 40,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fY0EQ_YFLMos"
},
"source": [
"# Define some hyperparameters\n",
"batch_size = 32\n",
"lr = 3e-4\n",
"n_epochs = 100"
],
"execution_count": 41,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FJCaeC67kaWS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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},
"outputId": "008de9b2-d540-4199-b410-e917e576d4cf"
},
"source": [
"# Create the dataset readers\n",
"train_dataset = ClassificationDatasetReader(train_data[:5000], bpemb_en)\n",
"# dataset loaded lazily with N workers in parallel\n",
"train_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_batch_bilstm, num_workers=8)\n",
"\n",
"valid_dataset = ClassificationDatasetReader(valid_data[:1000], bpemb_en)\n",
"valid_dl = DataLoader(valid_dataset, batch_size=len(valid_data[:1000]), collate_fn=collate_batch_bilstm, num_workers=8)\n",
"\n",
"# Create the optimizer\n",
"optimizer = Adam(model.parameters(), lr=lr)\n",
"\n",
"# Train\n",
"model, losses = train(model, train_dl, valid_dl, optimizer, n_epochs, device)"
],
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: 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:32: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
]
},
{
"output_type": "display_data",
"data": {
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},
{
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"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
" app.launch_new_instance()\n"
]
},
{
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{
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"text": [
"Validation accuracy: 0.712, train loss: 0.6779187424167706\n"
]
},
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]
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},
{
"cell_type": "markdown",
"metadata": {
"id": "s2Jiw-ssYc-u"
},
"source": [
"Next we can plot the loss curve"
]
},
{
"cell_type": "code",
"metadata": {
"id": "B-zuTelyYZgb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"outputId": "34dc3daf-b98a-4cd4-b57f-028978e6d4d0"
},
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(losses)"
],
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f7faa5ec5d0>]"
]
},
"metadata": {},
"execution_count": 43
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-7DsUVuVUzQ9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 223,
"referenced_widgets": [
"4cd86a2e259f4bb4be66e0aa16b43675",
"2921caf3c07d4db3a7de46d3e2b8552d",
"1e51ab37afae4cbebd77c021ef9426d4",
"a049035d322a44b9ac55ba3775ccbd1e",
"6b84c44c13ed4dad9fa7f37421aed8d7",
"ba8051aa69784cb082efc5bc43896428",
"977903dd87de4a7eb602be0880511056",
"c859bfa83c7740d3831fbffb0a4f9c86",
"9b9c550a5b7d483aaae0807d144abf39",
"65ebb81f4825430987cbb425d6f426a3",
"102ec22a2cab46dc9a0c1e5bdebdadc6",
"823e152e1d3148ab972c4f3a358894d9",
"6d552f26f7cb4ceeb6e7465c63a45d48",
"88d188fe4e8b4703a7cd936f6fbf0f37",
"1bf9366ae1a6432bb2275a182b905c9a",
"b401dc9563e94aef9e5e1cb894923cea",
"522d62d0b676442199eebdbbe1255b87",
"340edfc275864a2dbe9da7ee01f5489b",
"ed9776c7a5db45b08b0e667e322ed3cd",
"5ae4bf7ad2f040d3b80eff3f1a0e1cf4",
"91c4565c246541d1bd2b94f266743f1a",
"8e8ffa4cfa834226b7354689619b572e"
]
},
"outputId": "6dd01d2e-2bbd-4559-fca6-86322b09c7d3"
},
"source": [
"test_dataset = ClassificationDatasetReader(test_data[:1000], bpemb_en)\n",
"test_dl = DataLoader(test_dataset, batch_size=len(test_data), collate_fn=collate_batch_bilstm, num_workers=8)\n",
"\n",
"val_acc,_,_ = evaluate(model, valid_dl)\n",
"test_acc,labs,logs = evaluate(model, test_dl)\n",
"print(f\"Valiation accuracy: {val_acc}\")\n",
"print(f\"Test accuracy: {test_acc}\")"
],
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: 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:16: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
" app.launch_new_instance()\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Evaluation: 0%| | 0/1 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4cd86a2e259f4bb4be66e0aa16b43675"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Evaluation: 0%| | 0/1 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "823e152e1d3148ab972c4f3a358894d9"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Valiation accuracy: 0.864\n",
"Test accuracy: 0.839\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LlpMYbkrNOjD"
},
"source": [
"References \n",
"- Introduction to Pytorch notebook from Stanford: https://nbviewer.jupyter.org/github/cgpotts/cs224u/blob/master/tutorial_pytorch.ipynb"
]
}
]
}