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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "215a1aae",
"metadata": {
"executionInfo": {
"elapsed": 128,
"status": "ok",
"timestamp": 1682285319377,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": 240
},
"id": "215a1aae"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-04-23 18:07:24.557548: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-04-23 18:07:25.431969: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
"source": [
"import torch\n",
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"import pandas as pd\n",
"\n",
"from transformers import BertTokenizerFast, BertForSequenceClassification\n",
"from transformers import Trainer, TrainingArguments"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "J5Tlgp4tNd0U",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 1897,
"status": "ok",
"timestamp": 1682285321454,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": 240
},
"id": "J5Tlgp4tNd0U",
"outputId": "3c9f0c5b-7bc3-4c15-c5ff-0a77d3b3b607"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
"- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"model_name = \"bert-base-uncased\"\n",
"tokenizer = BertTokenizerFast.from_pretrained(model_name)\n",
"model = BertForSequenceClassification.from_pretrained(model_name, num_labels=6)\n",
"max_len = 200\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"results\",\n",
" num_train_epochs=1,\n",
" per_device_train_batch_size=16,\n",
" per_device_eval_batch_size=64,\n",
" warmup_steps=500,\n",
" learning_rate=5e-5,\n",
" weight_decay=0.01,\n",
" logging_dir=\"./logs\",\n",
" logging_steps=10\n",
" )\n",
"\n",
"# dataset class that inherits from torch.utils.data.Dataset\n",
"class TweetDataset(Dataset):\n",
" def __init__(self, encodings, labels):\n",
" self.encodings = encodings\n",
" self.labels = labels\n",
" self.tok = tokenizer\n",
" \n",
" def __getitem__(self, idx):\n",
" # encoding = self.tok(self.encodings[idx], truncation=True, padding=\"max_length\", max_length=max_len)\n",
" item = { key: torch.tensor(val[idx]) for key, val in self.encoding.items() }\n",
" item['labels'] = torch.tensor(self.labels[idx])\n",
" return item\n",
" \n",
" def __len__(self):\n",
" return len(self.labels)\n",
" \n",
"class TokenizerDataset(Dataset):\n",
" def __init__(self, strings):\n",
" self.strings = strings\n",
" \n",
" def __getitem__(self, idx):\n",
" return self.strings[idx]\n",
" \n",
" def __len__(self):\n",
" return len(self.strings)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9969c58c",
"metadata": {
"executionInfo": {
"elapsed": 5145,
"status": "ok",
"timestamp": 1682285326593,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": 240
},
"id": "9969c58c",
"scrolled": false
},
"outputs": [],
"source": [
"train_data = pd.read_csv(\"data/train.csv\")\n",
"train_text = train_data[\"comment_text\"]\n",
"train_labels = train_data[[\"toxic\", \"severe_toxic\", \n",
" \"obscene\", \"threat\", \n",
" \"insult\", \"identity_hate\"]]\n",
"\n",
"test_text = pd.read_csv(\"data/test.csv\")[\"comment_text\"]\n",
"test_labels = pd.read_csv(\"data/test_labels.csv\")[[\n",
" \"toxic\", \"severe_toxic\", \n",
" \"obscene\", \"threat\", \n",
" \"insult\", \"identity_hate\"]]\n",
"\n",
"# data preprocessing\n",
"\n",
"\n",
"\n",
"train_text = train_text.values.tolist()\n",
"train_labels = train_labels.values.tolist()\n",
"test_text = test_text.values.tolist()\n",
"test_labels = test_labels.values.tolist()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1n56TME9Njde",
"metadata": {
"executionInfo": {
"elapsed": 12,
"status": "ok",
"timestamp": 1682285326594,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": 240
},
"id": "1n56TME9Njde"
},
"outputs": [],
"source": [
"# prepare tokenizer and dataset\n",
"\n",
"train_strings = TokenizerDataset(train_text)\n",
"test_strings = TokenizerDataset(test_text)\n",
"\n",
"train_dataloader = DataLoader(train_strings, batch_size=16, shuffle=True)\n",
"test_dataloader = DataLoader(test_strings, batch_size=16, shuffle=True)\n",
"\n",
"\n",
"\n",
"\n",
"# train_encodings = tokenizer.batch_encode_plus(train_text, \\\n",
"# max_length=200, pad_to_max_length=True, \\\n",
"# truncation=True, return_token_type_ids=False \\\n",
"# )\n",
"# test_encodings = tokenizer.batch_encode_plus(test_text, \\\n",
"# max_length=200, pad_to_max_length=True, \\\n",
"# truncation=True, return_token_type_ids=False \\\n",
"# )\n",
"\n",
"\n",
"train_encodings = tokenizer(train_text, truncation=True, padding=True)\n",
"test_encodings = tokenizer(test_text, truncation=True, padding=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5c7a657",
"metadata": {},
"outputs": [],
"source": [
"f = open(\"traintokens.txt\", 'a')\n",
"f.write(train_encodings)\n",
"f.write('\\n\\n\\n\\n\\n')\n",
"f.close()\n",
"\n",
"g = open(\"testtokens.txt\", 'a')\n",
"g.write(test_encodings)\n",
"g.write('\\n\\n\\n\\n\\n')\n",
"\n",
"g.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4kwydz67qjW9",
"metadata": {
"executionInfo": {
"elapsed": 10,
"status": "ok",
"timestamp": 1682285326595,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": 240
},
"id": "4kwydz67qjW9"
},
"outputs": [],
"source": [
"train_dataset = TweetDataset(train_ecnodings, train_labels)\n",
"test_dataset = TweetDataset(test_encodings, test_labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "krZKjDVwNnWI",
"metadata": {
"executionInfo": {
"elapsed": 10,
"status": "ok",
"timestamp": 1682285326596,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": 240
},
"id": "krZKjDVwNnWI"
},
"outputs": [],
"source": [
"# training\n",
"trainer = Trainer(\n",
" model=model, \n",
" args=training_args, \n",
" train_dataset=train_dataset, \n",
" eval_dataset=test_dataset\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "VwsyMZg_tgTg",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 416
},
"executionInfo": {
"elapsed": 27193,
"status": "error",
"timestamp": 1682285353779,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": 240
},
"id": "VwsyMZg_tgTg",
"outputId": "49c3f5c8-0342-45c5-8d0f-5cd5d2d1f9e9"
},
"outputs": [],
"source": [
"trainer.train()"
]
}
],
"metadata": {
"colab": {
"provenance": [
{
"file_id": "https://github.com/joebraha/aiproject/blob/milestone-3/training.ipynb",
"timestamp": 1682285843150
}
]
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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",
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
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