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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BERT Predict classification\n",
"\n",
"## 1. Setup the environment\n",
"\n",
"### 1.1 Setup colab environment\n",
"\n",
"#### 1.1.1 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": {},
"source": [
"#### 1.1.2 Use more RAM"
]
},
{
"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": "markdown",
"metadata": {},
"source": [
"#### 1.1.3 Mount GoogleDrive"
]
},
{
"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": "wSqbrupGMc1M"
},
"source": [
"### 1.2 Import librairies"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SkErnwgMMbRj"
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd \n",
"import numpy as np\n",
"import pickle \n",
"import torch\n",
"from tqdm import tqdm\n",
"\n",
"from transformers import BertTokenizer, BertForSequenceClassification, CamembertTokenizer, CamembertForSequenceClassification\n",
"from torch.utils.data import TensorDataset, DataLoader, SequentialSampler"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8hzEGHl7gmzk"
},
"source": [
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dPOU-Efhf4ui",
"outputId": "121dd21e-f98c-483d-d6d1-2838f732a4e2"
},
"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",
"# 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",
"def generate_dataloader(tokenizer, sentences, batch_size = 8, max_len = 512):\n",
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" # 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:\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",
" # 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 = max_len, # Truncate all sentences.\n",
" #return_tensors = 'pt', # Return pytorch tensors.\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",
" if len(i) > max_len:\n",
" padded_test.extend([i[:max_len]])\n",
" else:\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",
" inputs = torch.tensor(input_ids_test)\n",
" masks = torch.tensor(attention_masks)\n",
" #set batch size\n",
"\n",
" # Create the DataLoader.\n",
" data = TensorDataset(inputs, masks)\n",
" prediction_sampler = SequentialSampler(data)\n",
"\n",
" return DataLoader(data, sampler=prediction_sampler, batch_size=batch_size)\n",
"\n",
"\n",
"def predict(model, dataloader, device):\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 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",
" 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",
" pred_labels = []\n",
" \n",
" for i in range(len(predictions_test)):\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",
"\n",
"\n",
"def text_folder_to_dataframe(path):\n",
"\n",
" data = []\n",
" # id,tome,filename,nb_words,content,domain\n",
"\n",
" for tome in sorted(os.listdir(path)):\n",
" try:\n",
" for article in tqdm(sorted(os.listdir(path + \"/\" + tome))):\n",
" filename = article[:-4]\n",
" id = tome + filename\n",
"\n",
" if article[-4:] == \".txt\":\n",
" with open(path + \"/\" + tome + \"/\" + article) as f:\n",
" content = f.read()\n",
"\n",
" data.append([id, tome, filename, content, len(content.split(' '))])\n",
" except NotADirectoryError:\n",
" pass\n",
" return pd.DataFrame(data, columns=['id', 'tome', 'filename', 'content', 'nb_words'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c5QKcXulhNJ-"
},
"source": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 LGE (Nakala)"
]
},
"metadata": {},
"outputs": [],
"source": [
"!wget https://api.nakala.fr/data/10.34847/nkl.74eb1xfd/e522413b58b04ab7c283f8fa68642e9cb69ab5c5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!unzip e522413b58b04ab7c283f8fa68642e9cb69ab5c5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"input_path = \"/Users/lmoncla/Documents/Data/Corpus/LGE/Text\"\n",
"#input_path = \"./Text\""
"metadata": {},
"outputs": [],
"source": [
"df_LGE = text_folder_to_dataframe(input_path)\n",
"#df_LGE = pd.read_csv(path + \"data/LGE_withContent.tsv\", sep=\"\\t\")\n",
"data_LGE = df_LGE[\"content\"].values"
]
},
{
"cell_type": "code",
"source": [
"df_LGE.head()"
]
},
{
"cell_type": "code",
"source": [
"df_LGE.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Load model and predict\n",
"\n",
"### 3.1 BERT / CamemBERT"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"#path = \"drive/MyDrive/Classification-EDdA/\"\n",
"path = \"../\"\n",
"model_name = \"bert-base-multilingual-cased\"\n",
"model_path = path + \"models/model_\" + model_name + \"_s10000.pt\""
]
},
{
"cell_type": "code",
"print('Loading Bert Tokenizer...')\n",
"tokenizer = BertTokenizer.from_pretrained(model_name)"
"source": [
"data_loader = generate_dataloader(tokenizer, data_LGE)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"model = BertForSequenceClassification.from_pretrained(model_path).to(gpu_name) #.to(\"cuda\")"
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_fzgS5USJeAF",
"outputId": "be4a5506-76ed-4eef-bb3c-fe2bb77c6e4d"
},
"outputs": [],
"source": [
"pred = predict(model, data_loader, device)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"encoder_filename = \"models/label_encoder.pkl\"\n",
"with open(path + encoder_filename, 'rb') as file:\n",
" encoder = pickle.load(file)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"p2 = list(encoder.inverse_transform(pred))"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"source": [
"df_LGE.head(50)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"filepath = path + \"results_LGE/LGE-metadata-withContent.csv\"\n",
"df_LGE.to_csv(filepath, sep=\"\\,\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_LGE.drop(columns=['content'], inplace=True)\n",
"filepath = path + \"results_LGE/LGE-metadata.csv\"\n",
"df_LGE.to_csv(filepath, sep=\"\\,\")"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"machine_shape": "hm",
"name": "EDdA-Classification_BertFineTuning.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3.9.13 ('geode-classification-py39')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"hash": "16fac9c2d845f8e1f8c6fffffe3d3a0def61c7e42da17a08d00f279ad4dea797"
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