{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "4YCMlsNwOWs0"
},
"source": [
"# BERT fine-tuning for EDdA classification"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6xdYI9moOQSv"
},
"source": [
"## Setup colab environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WF0qFN_g3ekz",
"outputId": "f3a5f049-24ee-418f-fe5e-84c633234ad8"
},
"outputs": [],
"source": [
"from psutil import virtual_memory\n",
"ram_gb = virtual_memory().total / 1e9\n",
"print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
"\n",
"if ram_gb < 20:\n",
" print('Not using a high-RAM runtime')\n",
"else:\n",
" print('You are using a high-RAM runtime!')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vL0S-s9Uofvn",
"outputId": "4b7efa4d-7f09-4c8e-bc98-99e6099ede32"
},
"outputs": [],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8hzEGHl7gmzk"
},
"source": [
"## Setup GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dPOU-Efhf4ui",
"outputId": "121dd21e-f98c-483d-d6d1-2838f732a4e2"
},
"outputs": [],
"source": [
"import torch\n",
"\n",
"# If there's a GPU available...\n",
"if torch.cuda.is_available(): \n",
" # Tell PyTorch to use the GPU. \n",
" device = torch.device(\"cuda\")\n",
" print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
" print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
"\n",
"# for MacOS\n",
"elif torch.backends.mps.is_available() and torch.backends.mps.is_built():\n",
" device = torch.device(\"mps\")\n",
" print('We will use the GPU')\n",
"else:\n",
" device = torch.device(\"cpu\")\n",
" print('No GPU available, using the CPU instead.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Jr-S9yYIgGkA"
},
"source": [
"## Install packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pwmZ5bBvgGNh",
"outputId": "fce0a8bf-1779-4079-c7ac-200ebb2678c5"
},
"outputs": [],
"source": [
"!pip install transformers==4.10.3\n",
"!pip install sentencepiece"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wSqbrupGMc1M"
},
"source": [
"## Import librairies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SkErnwgMMbRj"
},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"import csv\n",
"import os\n",
"import pickle\n",
"from sklearn import preprocessing\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import *\n",
"\n",
"from transformers import BertTokenizer, CamembertTokenizer, BertForSequenceClassification, AdamW, BertConfig, CamembertForSequenceClassification\n",
"from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
"from transformers import get_linear_schedule_with_warmup\n",
"\n",
"import time\n",
"import datetime\n",
"\n",
"import random\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import plot_confusion_matrix\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import classification_report\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "12SA-qPFgsVo"
},
"source": [
"## Utils functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WkIVcabUgxIl"
},
"outputs": [],
"source": [
"def resample_classes(df, classColumnName, numberOfInstances):\n",
" #random numberOfInstances elements\n",
" replace = False # with replacement\n",
" fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]\n",
" return df.groupby(classColumnName, as_index=False).apply(fn)\n",
" \n",
"# Function to calculate the accuracy of our predictions vs labels\n",
"def flat_accuracy(preds, labels):\n",
" pred_flat = np.argmax(preds, axis=1).flatten()\n",
" labels_flat = labels.flatten()\n",
" return np.sum(pred_flat == labels_flat) / len(labels_flat) \n",
"\n",
"def format_time(elapsed):\n",
" '''\n",
" Takes a time in seconds and returns a string hh:mm:ss\n",
" '''\n",
" # Round to the nearest second.\n",
" elapsed_rounded = int(round((elapsed)))\n",
"\n",
" # Format as hh:mm:ss\n",
" return str(datetime.timedelta(seconds=elapsed_rounded))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c5QKcXulhNJ-"
},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jdCdUVOTZrqh",
"outputId": "ac52be55-ed4b-4c50-dc8c-9124ca6b71e5"
},
"outputs": [],
"source": [
"!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/training_set.tsv\n",
"!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/test_set.tsv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/training_set_superdomains.tsv\n",
"!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/test_set_superdomains.tsv"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9d1IxD_bLEvp"
},
"source": [
"### Loading dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7JEnKknRoClH"
},
"outputs": [],
"source": [
"#train_path = '../data/training_set.tsv'\n",
"#test_path = '../data/test_set.tsv'\n",
"\n",
"train_path = '../data/training_set_superdomains.tsv'\n",
"test_path = '../data/test_set_superdomains.tsv'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
},
"id": "5u1acjunhoxe",
"outputId": "3038048d-6506-473d-85c9-2d3ebdcc6a72"
},
"outputs": [],
"source": [
"df_train = pd.read_csv(train_path, sep=\"\\t\")\n",
"df_train.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zj3JDoJNfx1f",
"outputId": "f1ec1fcf-b287-460a-8110-dbb00091c203"
},
"outputs": [],
"source": [
"print(df_train.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dADYGtTcn4AB"
},
"source": [
"## Configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "I0OrfFsBn4Io"
},
"outputs": [],
"source": [
"columnText = 'contentWithoutClass'\n",
"#columnClass = 'ensemble_domaine_enccre'\n",
"columnClass = 'super_domain'\n",
"\n",
"maxOfInstancePerClass = 10000\n",
"\n",
"model_chosen = \"bert\"\n",
"#model_chosen = \"camembert\"\n",
"\n",
"batch_size = 16 # 16 or 32 recommended\n",
"max_len = 512\n",
"\n",
"#path = \"drive/MyDrive/Classification-EDdA/\"\n",
"path = \"../models/new/\"\n",
"encoder_filename = \"label_encoder.pkl\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "t3brU-Yvn4XS"
},
"source": [
"## Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aQCLJE4Jtm7v"
},
"outputs": [],
"source": [
"if maxOfInstancePerClass != 10000:\n",
" df_train = resample_classes(df_train, columnClass, maxOfInstancePerClass)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zrjZvs2dhzAy"
},
"outputs": [],
"source": [
"labels = df_train[columnClass]\n",
"numberOfClasses = labels.nunique()\n",
"\n",
"\n",
"if os.path.isfile(path+encoder_filename): \n",
" # load existing encoder \n",
" with open(path+encoder_filename, 'rb') as file:\n",
" encoder = pickle.load(file)\n",
"\n",
"else:\n",
" encoder = preprocessing.LabelEncoder()\n",
" encoder.fit(labels)\n",
"\n",
" with open(path+encoder_filename, 'wb') as file:\n",
" pickle.dump(encoder, file)\n",
"\n",
"\n",
"labels = encoder.transform(labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Xt_PhH_6h1_3"
},
"outputs": [],
"source": [
"sentences_train = df_train[columnText].values\n",
"labels_train = labels.tolist()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Dq_KF5WAsbpC",
"outputId": "7925ce5a-4b9e-4147-fdc1-f2916d0e6600"
},
"outputs": [],
"source": [
"sentences_train"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gs4Agx_5h43M"
},
"source": [
"# Model\n",
"## Tokenisation & Input Formatting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YZ5PhEYZiCEA"
},
"outputs": [],
"source": [
"if model_chosen == \"bert\":\n",
" tokeniser_bert = 'bert-base-multilingual-cased'\n",
" model_bert = \"bert-base-multilingual-cased\"\n",
"elif model_chosen == \"camembert\":\n",
" tokeniser_bert = 'camembert-base'\n",
" model_bert = 'camembert-base'\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 131,
"referenced_widgets": [
"274e505b5f354efc8de3ef26cc43e617",
"f1f9d5b32f60473b86ae6b340d6c0850",
"ad5e0e1439a94578a31b80c90dbf3247",
"0779c8ea0ed24e64a800ae5dff6bc8ce",
"7870340ac12b469c8ac19de3a47b6e67",
"5f321455342348f49879a9ca8b392077",
"9420a47a2bf44ead8cff283f20566cda",
"99b785ea53744868b8b11e5e94936fcc",
"8d24b669a39b4876ac0a014dff678db1",
"2cf386a8d14d43389374f79bfa922675",
"2c44d9c11e704b70aa32904a23d1790c",
"0279837673b446b09aac18346213eb7e",
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"69004a5069094f8c9d59d5136f627bef",
"e96a95317b0945c58c8ff0e944c7593e",
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"d4ad1a78750d49feaea584a82940bb7d",
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"9383e09698ae4bd1820a4bca22e78315",
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"c4d981755d1d42b6940396b77bc251bc",
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"df95c20399dd4918bc7559a90886d4aa"
]
},
"id": "C4bigx_3ibuN",
"outputId": "ebcca5ee-85d8-4525-c9ad-9fc3b5c1741d"
},
"outputs": [],
"source": [
"# Load the BERT tokenizer.\n",
"if model_chosen == \"bert\":\n",
" print('Loading BERT tokenizer...')\n",
" tokenizer = BertTokenizer.from_pretrained(tokeniser_bert)\n",
"elif model_chosen == \"camembert\":\n",
" print('Loading CamemBERT tokenizer...')\n",
" tokenizer = CamembertTokenizer.from_pretrained(tokeniser_bert)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5hNod5X9jDZN",
"outputId": "bca0db0e-7463-40cd-8052-1712965c7a95"
},
"outputs": [],
"source": [
" # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
"input_ids_train = []\n",
"\n",
"# For every sentence...\n",
"for sent in sentences_train:\n",
" # `encode` will:\n",
" # (1) Tokenize the sentence.\n",
" # (2) Prepend the `[CLS]` token to the start.\n",
" # (3) Append the `[SEP]` token to the end.\n",
" # (4) Map tokens to their IDs.\n",
" encoded_sent_train = tokenizer.encode(\n",
" str(sent), # Sentence to encode.\n",
" add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
"\n",
" # This function also supports truncation and conversion\n",
" # to pytorch tensors, but I need to do padding, so I\n",
" # can't use these features.\n",
" #max_length = 128, # Truncate all sentences.\n",
" #return_tensors = 'pt', # Return pytorch tensors.\n",
" )\n",
" \n",
" # Add the encoded sentence to the list.\n",
" input_ids_train.append(encoded_sent_train)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "W9EWv5JvjGH3",
"outputId": "dde87708-7bcb-47c7-af71-2ec2b2e0c2db"
},
"outputs": [],
"source": [
"print('Max sentence length train: ', max([len(sen) for sen in input_ids_train]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xh1TQJyvjOx5"
},
"outputs": [],
"source": [
"\n",
"padded_train = []\n",
"for i in input_ids_train:\n",
"\n",
" if len(i) > max_len:\n",
" padded_train.extend([i[:max_len]])\n",
" else:\n",
" padded_train.extend([i + [0] * (max_len - len(i))])\n",
"\n",
"\n",
"padded_train = input_ids_train = np.array(padded_train)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZiwY6gn0jUkD"
},
"outputs": [],
"source": [
" # Create attention masks\n",
"attention_masks_train = []\n",
"\n",
"# For each sentence...\n",
"for sent in padded_train:\n",
" \n",
" # Create the attention mask.\n",
" # - If a token ID is 0, then it's padding, set the mask to 0.\n",
" # - If a token ID is > 0, then it's a real token, set the mask to 1.\n",
" att_mask = [int(token_id > 0) for token_id in sent]\n",
" \n",
" # Store the attention mask for this sentence.\n",
" attention_masks_train.append(att_mask)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oBTR5AfAjXJe"
},
"outputs": [],
"source": [
"# Use 70% for training and 30% for validation.\n",
"#train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(padded, labels, \n",
"# random_state=2018, test_size=0.3, stratify = labels)\n",
"# Do the same for the masks.\n",
"#train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels,\n",
"# random_state=2018, test_size=0.3, stratify = labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b9Mw5kq3jhTb"
},
"outputs": [],
"source": [
"# Convert all inputs and labels into torch tensors, the required datatype \n",
"# for my model.\n",
"train_inputs = torch.tensor(padded_train)\n",
"\n",
"train_labels = torch.tensor(labels_train)\n",
"\n",
"train_masks = torch.tensor(attention_masks_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UfFWzbENjnkw"
},
"outputs": [],
"source": [
"# The DataLoader needs to know the batch size for training, so I specify it here.\n",
"# For fine-tuning BERT on a specific task, the authors recommend a batch size of\n",
"# 16 or 32.\n",
"\n",
"# Create the DataLoader for training set.\n",
"train_data = TensorDataset(train_inputs, train_masks, train_labels)\n",
"train_sampler = RandomSampler(train_data)\n",
"train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x45JNGqhkUn2"
},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"d09d664839d04303b8fef9ef895f6e4f",
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},
"id": "C7M2Er1ajsTf",
"outputId": "151034cd-9a77-413e-a61e-561c97b4072e"
},
"outputs": [],
"source": [
"# Load BertForSequenceClassification, the pretrained BERT model with a single \n",
"# linear classification layer on top.\n",
"\n",
"#model = CamembertForSequenceClassification.from_pretrained(\n",
"if model_chosen == \"bert\":\n",
" model = BertForSequenceClassification.from_pretrained(\n",
" model_bert, # Use the 12-layer BERT model, with an uncased vocab.\n",
" num_labels = numberOfClasses, # The number of output labels--2 for binary classification.\n",
" # You can increase this for multi-class tasks. \n",
" output_attentions = False, # Whether the model returns attentions weights.\n",
" output_hidden_states = False, # Whether the model returns all hidden-states.\n",
" )\n",
"elif model_chosen == \"camembert\":\n",
" model = CamembertForSequenceClassification.from_pretrained(\n",
" model_bert, # Use the 12-layer BERT model, with an uncased vocab.\n",
" num_labels = numberOfClasses, # The number of output labels--2 for binary classification.\n",
" # You can increase this for multi-class tasks. \n",
" output_attentions = False, # Whether the model returns attentions weights.\n",
" output_hidden_states = False, # Whether the model returns all hidden-states.\n",
" )\n",
"\n",
"# Tell pytorch to run this model on the GPU.\n",
"#model.cuda()\n",
"model.to(\"mps\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xd_cG-8pj4Iw"
},
"outputs": [],
"source": [
"#Note: AdamW is a class from the huggingface library (as opposed to pytorch) \n",
"# I believe the 'W' stands for 'Weight Decay fix\"\n",
"optimizer = AdamW(model.parameters(),\n",
" lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n",
" eps = 1e-8 # args.adam_epsilon - default is 1e-8.\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "65G-uHuLj4_6"
},
"outputs": [],
"source": [
"# Number of training epochs (authors recommend between 2 and 4)\n",
"epochs = 4\n",
"\n",
"# Total number of training steps is number of batches * number of epochs.\n",
"total_steps = len(train_dataloader) * epochs\n",
"\n",
"# Create the learning rate scheduler.\n",
"scheduler = get_linear_schedule_with_warmup(optimizer, \n",
" num_warmup_steps = 0, # Default value in run_glue.py\n",
" num_training_steps = total_steps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SbHBbYpwkKaA",
"outputId": "4cd1be4a-6014-4804-df56-f38e98039797"
},
"outputs": [],
"source": [
"# This training code is based on the `run_glue.py` script here:\n",
"# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128\n",
"\n",
"# Set the seed value all over the place to make this reproducible.\n",
"seed_val = 42\n",
"\n",
"random.seed(seed_val)\n",
"np.random.seed(seed_val)\n",
"torch.manual_seed(seed_val)\n",
"torch.cuda.manual_seed_all(seed_val)\n",
"\n",
"# Store the average loss after each epoch so I can plot them.\n",
"loss_values = []\n",
"\n",
"# For each epoch...\n",
"for epoch_i in range(0, epochs):\n",
" \n",
" # ========================================\n",
" # Training\n",
" # ========================================\n",
" \n",
" # Perform one full pass over the training set.\n",
"\n",
" print(\"\")\n",
" print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
" print('Training...')\n",
"\n",
" # Measure how long the training epoch takes.\n",
" t0 = time.time()\n",
"\n",
" # Reset the total loss for this epoch.\n",
" total_loss = 0\n",
"\n",
" # Put the model into training mode.\n",
" model.train()\n",
"\n",
" # For each batch of training data...\n",
" for step, batch in enumerate(train_dataloader):\n",
"\n",
" # Progress update every 40 batches.\n",
" if step % 5 == 0 and not step == 0:\n",
" # Calculate elapsed time in minutes.\n",
" elapsed = format_time(time.time() - t0)\n",
" \n",
" # Report progress.\n",
" print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n",
"\n",
" # Unpack this training batch from the dataloader. \n",
" #\n",
" # As I unpack the batch, I'll also copy each tensor to the GPU using the \n",
" # `to` method.\n",
" #\n",
" # `batch` contains three pytorch tensors:\n",
" # [0]: input ids \n",
" # [1]: attention masks\n",
" # [2]: labels \n",
" b_input_ids = batch[0].to(device)\n",
" b_input_mask = batch[1].to(device)\n",
" b_labels = batch[2].to(device)\n",
"\n",
" # Always clear any previously calculated gradients before performing a\n",
" # backward pass. PyTorch doesn't do this automatically because \n",
" # accumulating the gradients is \"convenient while training RNNs\". \n",
" # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)\n",
" model.zero_grad() \n",
"\n",
" # Perform a forward pass (evaluate the model on this training batch).\n",
" # This will return the loss (rather than the model output) because I\n",
" # have provided the `labels`.\n",
" # The documentation for this `model` function is here: \n",
" # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
" outputs = model(b_input_ids, \n",
" token_type_ids=None, \n",
" attention_mask=b_input_mask, \n",
" labels=b_labels)\n",
" \n",
" # The call to `model` always returns a tuple, so I need to pull the \n",
" # loss value out of the tuple.\n",
" loss = outputs[0]\n",
"\n",
" # Accumulate the training loss over all of the batches so that I can\n",
" # calculate the average loss at the end. `loss` is a Tensor containing a\n",
" # single value; the `.item()` function just returns the Python value \n",
" # from the tensor.\n",
" total_loss += loss.item()\n",
"\n",
" # Perform a backward pass to calculate the gradients.\n",
" loss.backward()\n",
"\n",
" # Clip the norm of the gradients to 1.0.\n",
" # This is to help prevent the \"exploding gradients\" problem.\n",
" torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
"\n",
" # Update parameters and take a step using the computed gradient.\n",
" # The optimizer dictates the \"update rule\"--how the parameters are\n",
" # modified based on their gradients, the learning rate, etc.\n",
" optimizer.step()\n",
"\n",
" # Update the learning rate.\n",
" scheduler.step()\n",
"\n",
" # Calculate the average loss over the training data.\n",
" avg_train_loss = total_loss / len(train_dataloader) \n",
" \n",
" # Store the loss value for plotting the learning curve.\n",
" loss_values.append(avg_train_loss)\n",
"\n",
" print(\"\")\n",
" print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\n",
" print(\" Training epoch took: {:}\".format(format_time(time.time() - t0)))\n",
" \n",
"print(\"\")\n",
"print(\"Training complete!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uEe7lPtVKpIY"
},
"source": [
"## Saving model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AYCSVm_wKnuM"
},
"outputs": [],
"source": [
"name = model_bert + \"_s\" + str(maxOfInstancePerClass)\n",
"model_path = path + \"model_\"+name+\".pt\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qmsxrOqjCsGo"
},
"outputs": [],
"source": [
"#torch.save(model, model_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.save_pretrained(model_path)\n",
"#ludo: changement de la façon de sauver le modèle"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pM9bSsckCndR"
},
"source": [
"## Loading model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cEycmiS8Cnjw"
},
"outputs": [],
"source": [
"#model = torch.load(model_path)\n",
"model = BertForSequenceClassification.from_pretrained(model_path).to(\"mps\") #.to(\"cuda\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VJwyfmakkQyj"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "K9qdtYexIIvk"
},
"outputs": [],
"source": [
"def evaluate_bert(data, labels, model, batch_size):\n",
" # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
" input_ids = []\n",
" # For every sentence...\n",
" for sent in data:\n",
" # `encode` will:\n",
" # (1) Tokenize the sentence.\n",
" # (2) Prepend the `[CLS]` token to the start.\n",
" # (3) Append the `[SEP]` token to the end.\n",
" # (4) Map tokens to their IDs.\n",
" encoded_sent = tokenizer.encode(\n",
" str(sent), # Sentence to encode.\n",
" add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
" )\n",
" \n",
" input_ids.append(encoded_sent)\n",
"\n",
" # Pad our input tokens\n",
" padded = []\n",
" for i in input_ids:\n",
"\n",
" if len(i) > max_len:\n",
" padded.extend([i[:max_len]])\n",
" else:\n",
" padded.extend([i + [0] * (max_len - len(i))])\n",
" input_ids = np.array(padded)\n",
"\n",
" # Create attention masks\n",
" attention_masks = []\n",
"\n",
" # Create a mask of 1s for each token followed by 0s for padding\n",
" for seq in input_ids:\n",
" seq_mask = [float(i>0) for i in seq]\n",
" attention_masks.append(seq_mask) \n",
"\n",
" # Convert to tensors.\n",
" prediction_inputs = torch.tensor(input_ids)\n",
" prediction_masks = torch.tensor(attention_masks)\n",
" prediction_labels = torch.tensor(labels)\n",
"\n",
" # Create the DataLoader.\n",
" prediction_data = TensorDataset(prediction_inputs, prediction_masks, prediction_labels)\n",
" prediction_sampler = SequentialSampler(prediction_data)\n",
" prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)\n",
"\n",
" print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs)))\n",
"\n",
" # Put model in evaluation mode\n",
" model.eval()\n",
"\n",
" # Tracking variables \n",
" predictions , true_labels = [], []\n",
"\n",
" # Predict \n",
" for batch in prediction_dataloader:\n",
" # Add batch to GPU\n",
" batch = tuple(t.to(device) for t in batch)\n",
" \n",
" # Unpack the inputs from the dataloader\n",
" b_input_ids, b_input_mask, b_labels = batch\n",
" \n",
" # Telling the model not to compute or store gradients, saving memory and \n",
" # speeding up prediction\n",
" with torch.no_grad():\n",
" # Forward pass, calculate logit predictions\n",
" outputs = model(b_input_ids, token_type_ids=None, \n",
" attention_mask=b_input_mask)\n",
"\n",
" logits = outputs[0]\n",
" #print(logits)\n",
"\n",
" # Move logits and labels to CPU\n",
" logits = logits.detach().cpu().numpy()\n",
" label_ids = b_labels.to('cpu').numpy()\n",
" #print(logits)\n",
" \n",
" # Store predictions and true labels\n",
" predictions.append(logits)\n",
" true_labels.append(label_ids)\n",
"\n",
" print(' DONE.')\n",
"\n",
"\n",
" pred_labels = []\n",
"\n",
" # Evaluate each test batch using many matrics\n",
" print('Calculating the matrics for each batch...')\n",
"\n",
" for i in range(len(true_labels)):\n",
" \n",
" # The predictions for this batch are a 2-column ndarray (one column for \"0\" \n",
" # and one column for \"1\"). Pick the label with the highest value and turn this\n",
" # in to a list of 0s and 1s.\n",
" pred_labels_i = np.argmax(predictions[i], axis=1).flatten()\n",
" pred_labels.append(pred_labels_i)\n",
"\n",
"\n",
" pred_labels_ = [item for sublist in pred_labels for item in sublist]\n",
" true_labels_ = [item for sublist in true_labels for item in sublist]\n",
"\n",
" return pred_labels_, true_labels_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dPjV_5g8DDQy"
},
"outputs": [],
"source": [
"dataset = \"test\"\n",
"\n",
"df_eval = pd.read_csv(dataset+\"_set.tsv\", sep=\"\\t\")\n",
"\n",
"data_eval = df_eval[columnText].values\n",
"\n",
"y = df_eval[columnClass]\n",
"\n",
"\n",
"\n",
"y = encoder.transform(y)\n",
"labels = y.tolist()\n",
"\n",
"\n",
"model_path = path+\"/model_\"+model_bert+\"_s\"+str(maxOfInstancePerClass)+\".pt\"\n",
"model = torch.load(model_path)\n",
"\n",
"if model_bert == \"bert-base-multilingual-cased\":\n",
" tokenizer = BertTokenizer.from_pretrained(model_bert)\n",
"elif model_bert == \"camembert-base\":\n",
" tokenizer = CamembertTokenizer.from_pretrained(model_bert)\n",
"\n",
"pred_labels_, true_labels_ = evaluate_bert(data_eval, labels, model, batch_size)\n",
"\n",
"\n",
"report = classification_report(true_labels_, pred_labels_, output_dict = True)\n",
" \n",
"classes = [str(e) for e in encoder.transform(encoder.classes_)]\n",
"classesName = encoder.classes_\n",
"\n",
"precision = []\n",
"recall = []\n",
"f1 = []\n",
"support = []\n",
"dff = pd.DataFrame(columns= ['className', 'precision', 'recall', 'f1-score', 'support', 'FP', 'FN', 'TP', 'TN'])\n",
"for c in classes:\n",
" precision.append(report[c]['precision'])\n",
" recall.append(report[c]['recall'])\n",
" f1.append(report[c]['f1-score'])\n",
" support.append(report[c]['support'])\n",
"\n",
"accuracy = report['accuracy']\n",
"weighted_avg = report['weighted avg']\n",
"cnf_matrix = confusion_matrix(true_labels_, pred_labels_)\n",
"FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)\n",
"FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)\n",
"TP = np.diag(cnf_matrix)\n",
"TN = cnf_matrix.sum() - (FP + FN + TP)\n",
"\n",
"dff['className'] = classesName\n",
"dff['precision'] = precision\n",
"dff['recall'] = recall\n",
"dff['f1-score'] = f1\n",
"dff['support'] = support\n",
"dff['FP'] = FP\n",
"dff['FN'] = FN\n",
"dff['TP'] = TP\n",
"dff['TN'] = TN\n",
"\n",
"print(name)\n",
"\n",
"name = \"test_\"+ name\n",
"content = name + \"\\n\"\n",
"print(name)\n",
"content += str(weighted_avg) + \"\\n\"\n",
"\n",
"print(weighted_avg)\n",
"print(accuracy)\n",
"print(dff)\n",
"\n",
"dff.to_csv(path+\"/report_\"+name+\".csv\", index=False)\n",
"# enregistrer les predictions\n",
"pd.DataFrame({'labels': pd.Series(true_labels_), 'predictions': pd.Series(pred_labels_)}).to_csv(path+\"/predictions/predictions_\"+name+\".csv\")\n",
"\n",
"with open(path+\"reports/report_\"+name+\".txt\", 'w') as f:\n",
" f.write(content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cVdM4eT6I8g2"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HzxyFO3knanV"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KDRPPw4Wnap7"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DX81R2dcnasF"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wgfqJFVeJMK1"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GqEf5_41JMNZ"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x_n57EvhJMQh"
},
"outputs": [],
"source": [
"model_path = \"drive/MyDrive/Classification-EDdA/model_bert-base-multilingual-cased_s10000.pt\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R3_9tA9MI8ju"
},
"outputs": [],
"source": [
"model = torch.load(model_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_fzgS5USJeAF",
"outputId": "be4a5506-76ed-4eef-bb3c-fe2bb77c6e4d"
},
"outputs": [],
"source": [
"!wget https://projet.liris.cnrs.fr/geode/files/datasets/EDdA/Classification/LGE_withContent.tsv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8WEJjQC7I8mP"
},
"outputs": [],
"source": [
"df_LGE = pd.read_csv(\"LGE_withContent.tsv\", sep=\"\\t\")\n",
"data_LGE = df_LGE[\"content\"].values\n",
"\n",
"\n",
"#pred_labels_, true_labels_ = evaluate_bert(data_eval, labels, model, batch_size)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "9qJDTU-6vzkk",
"outputId": "1b279f0e-7715-4d23-f524-08e8ba327f6c"
},
"outputs": [],
"source": [
"df_LGE.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "71-fP61-OOwQ",
"outputId": "ef08b49e-0a9f-4653-e303-3163250af35b"
},
"outputs": [],
"source": [
"df_LGE.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lFFed2EAI8oq"
},
"outputs": [],
"source": [
"def generate_prediction_dataloader(chosen_model, sentences_to_predict, batch_size = 8, max_len = 512):\n",
"\n",
" if chosen_model == 'bert-base-multilingual-cased' :\n",
" print('Loading Bert Tokenizer...')\n",
" tokenizer = BertTokenizer.from_pretrained(chosen_model)\n",
" elif chosen_model == 'camembert-base':\n",
" print('Loading Camembert Tokenizer...')\n",
" tokenizer = CamembertTokenizer.from_pretrained(chosen_model)\n",
"\n",
" # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
" input_ids_test = []\n",
" # For every sentence...\n",
" for sent in sentences_to_predict:\n",
" # `encode` will:\n",
" # (1) Tokenize the sentence.\n",
" # (2) Prepend the `[CLS]` token to the start.\n",
" # (3) Append the `[SEP]` token to the end.\n",
" # (4) Map tokens to their IDs.\n",
" encoded_sent = tokenizer.encode(\n",
" sent, # Sentence to encode.\n",
" add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
" )\n",
"\n",
" input_ids_test.append(encoded_sent)\n",
"\n",
" # Pad our input tokens\n",
" padded_test = []\n",
" for i in input_ids_test:\n",
"\n",
" if len(i) > max_len:\n",
" padded_test.extend([i[:max_len]])\n",
" else:\n",
"\n",
" padded_test.extend([i + [0] * (max_len - len(i))])\n",
" input_ids_test = np.array(padded_test)\n",
"\n",
" # Create attention masks\n",
" attention_masks = []\n",
"\n",
" # Create a mask of 1s for each token followed by 0s for padding\n",
" for seq in input_ids_test:\n",
" seq_mask = [float(i>0) for i in seq]\n",
" attention_masks.append(seq_mask)\n",
"\n",
" # Convert to tensors.\n",
" prediction_inputs = torch.tensor(input_ids_test)\n",
" prediction_masks = torch.tensor(attention_masks)\n",
" #set batch size\n",
"\n",
"\n",
" # Create the DataLoader.\n",
" prediction_data = TensorDataset(prediction_inputs, prediction_masks)\n",
" prediction_sampler = SequentialSampler(prediction_data)\n",
" prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)\n",
"\n",
" return prediction_dataloader\n",
"\n",
"\n",
"\n",
"def predict_class_bertFineTuning(model, sentences_to_predict_dataloader):\n",
"\n",
"\n",
" # If there's a GPU available...\n",
" if torch.cuda.is_available():\n",
"\n",
" # Tell PyTorch to use the GPU.\n",
" device = torch.device(\"cuda\")\n",
"\n",
" print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
"\n",
" print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
"\n",
" # If not...\n",
" else:\n",
" print('No GPU available, using the CPU instead.')\n",
" device = torch.device(\"cpu\")\n",
"\n",
" # Put model in evaluation mode\n",
" model.eval()\n",
"\n",
" # Tracking variables\n",
" predictions_test , true_labels = [], []\n",
" pred_labels_ = []\n",
" # Predict\n",
" for batch in sentences_to_predict_dataloader:\n",
" # Add batch to GPU\n",
" batch = tuple(t.to(device) for t in batch)\n",
"\n",
" # Unpack the inputs from the dataloader\n",
" b_input_ids, b_input_mask = batch\n",
"\n",
" # Telling the model not to compute or store gradients, saving memory and\n",
" # speeding up prediction\n",
" with torch.no_grad():\n",
" # Forward pass, calculate logit predictions\n",
" outputs = model(b_input_ids, token_type_ids=None,\n",
" attention_mask=b_input_mask)\n",
"\n",
" logits = outputs[0]\n",
" #print(logits)\n",
"\n",
" # Move logits and labels to CPU\n",
" logits = logits.detach().cpu().numpy()\n",
" #print(logits)\n",
"\n",
" # Store predictions and true labels\n",
" predictions_test.append(logits)\n",
"\n",
" #print(' DONE.')\n",
"\n",
" pred_labels = []\n",
" \n",
" for i in range(len(predictions_test)):\n",
"\n",
" # The predictions for this batch are a 2-column ndarray (one column for \"0\"\n",
" # and one column for \"1\"). Pick the label with the highest value and turn this\n",
" # in to a list of 0s and 1s.\n",
" pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()\n",
" pred_labels.append(pred_labels_i)\n",
"\n",
" pred_labels_ += [item for sublist in pred_labels for item in sublist]\n",
" return pred_labels_\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O9eer_kgI8rC",
"outputId": "94ea7418-14a8-4918-e210-caf0018f5989"
},
"outputs": [],
"source": [
"data_loader = generate_prediction_dataloader('bert-base-multilingual-cased', data_LGE)\n",
"#data_loader = generate_prediction_dataloader('camembert-base', data_LGE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sFpAwbrBwF2h",
"outputId": "8d210732-619d-41f0-b6e2-ad9d06a85069"
},
"outputs": [],
"source": [
"p = predict_class_bertFineTuning( model, data_loader )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "51HF6-8UPSTc",
"outputId": "26bff792-eb8d-4e1a-efa4-a7a6c9d32bf9"
},
"outputs": [],
"source": [
"len(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rFFGhaCvQHfh"
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