Text Classification Using BERT
T
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"#Text Classification using BERT\n",
"\n",
"The is a basic implementation of text classification pipeline using BERT. The BERT model used has been taken from [huggingface](https://huggingface.co/transformers/). The dataset used is a custom dataset with two classes (labelled as 0 and 1). It is publically available [here](https://raw.githubusercontent.com/prateekjoshi565/Fine-Tuning-BERT/master/spamdata_v2.csv)."
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"!pip install transformers"
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"text": [
"Collecting transformers\n",
" Downloading transformers-4.10.0-py3-none-any.whl (2.8 MB)\n",
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"Collecting huggingface-hub>=0.0.12\n",
" Downloading huggingface_hub-0.0.16-py3-none-any.whl (50 kB)\n",
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"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
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"Collecting tokenizers<0.11,>=0.10.1\n",
" Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
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"Collecting sacremoses\n",
" Downloading sacremoses-0.0.45-py3-none-any.whl (895 kB)\n",
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"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
"Installing collected packages: tokenizers, sacremoses, pyyaml, huggingface-hub, transformers\n",
" Attempting uninstall: pyyaml\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
"Successfully installed huggingface-hub-0.0.16 pyyaml-5.4.1 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.10.0\n"
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"source": [
"import csv\n",
"import pickle\n",
"import pandas as pd\n",
"import numpy as np\n",
"train = pd.read_csv(\"spamdata_v2.csv\")\n"
],
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"source": [
"print(len(train))\n",
"train.head()"
],
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"5572\n"
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
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" label text\n",
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"id": "mxdJUOsolsMj"
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"num_classes = 2"
],
"id": "mxdJUOsolsMj",
"execution_count": 3,
"outputs": []
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"tags": [],
"id": "eb38df80"
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"train_split, val_split = train_test_split(train, test_size=.05)"
],
"id": "eb38df80",
"execution_count": 4,
"outputs": []
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"tags": [],
"id": "a9bbf53d"
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"source": [
"from transformers import BertTokenizerFast\n",
"tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')"
],
"id": "a9bbf53d",
"execution_count": 5,
"outputs": []
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"source": [
"import torch\n",
"\n",
"class Dataset(torch.utils.data.Dataset):\n",
" def __init__(self, df, tokenizer, max_length=128):\n",
" self.df = df\n",
" self.text = df.text.values\n",
" self.labels = df.label.values\n",
" self.tokenizer = tokenizer\n",
" self.max_length = max_length\n",
" \n",
" def __getitem__(self, idx):\n",
" \n",
" tokenized_data = tokenizer.tokenize(self.text[idx])\n",
" to_append = [\"[CLS]\"] + tokenized_data[:self.max_length - 2] + [\"[SEP]\"]\n",
" input_ids = tokenizer.convert_tokens_to_ids(to_append)\n",
" input_mask = [1] * len(input_ids)\n",
" padding = [0] * (self.max_length - len(input_ids))\n",
" input_ids += padding\n",
" input_mask += padding\n",
" item = {\n",
" \"input_ids\": torch.tensor(input_ids, dtype=torch.long),\n",
" \"attention_mask\": torch.tensor(input_mask, dtype=torch.long)\n",
" }\n",
" item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n",
" return item\n",
" \n",
" def __len__(self):\n",
" return len(self.df)\n",
"\n",
"train_dataset = Dataset(train_split.fillna(\"\"), tokenizer)\n",
"val_dataset = Dataset(val_split.fillna(\"\"), tokenizer)\n",
"# train_dataset = Dataset(train.fillna(\"\"), tokenizer, is_train=True, label_map=label_map)\n",
"# test_dataset = Dataset(test.fillna(\"\"), tokenizer, is_train=False)"
],
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},
"id": "6186779d",
"outputId": "9e0874d1-9977-4b48-9c98-3d19e5d4ccd0"
},
"source": [
"from transformers import BertForSequenceClassification, Trainer, TrainingArguments\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir='./results', # output directory\n",
" num_train_epochs=50, # total number of training epochs\n",
" per_device_train_batch_size=64, # batch size per device during training\n",
" per_device_eval_batch_size=64, # batch size for evaluation\n",
" warmup_steps=500, # number of warmup steps for learning rate scheduler\n",
" weight_decay=0.01, # strength of weight decay\n",
" logging_dir='./logs', # directory for storing logs\n",
" logging_steps=100,\n",
" dataloader_num_workers=2,\n",
" report_to=\"tensorboard\",\n",
" label_smoothing_factor=0.1,\n",
" evaluation_strategy=\"steps\",\n",
" eval_steps=500, # Evaluation and Save happens every 500 steps\n",
" save_total_limit=3, # Only last 5 models are saved. Older ones are deleted.\n",
" load_best_model_at_end=True, #best model is always saved\n",
")\n",
"\n",
"model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\n",
"model.classifier = torch.nn.Linear(768, num_classes)\n",
"model.num_labels = num_classes"
],
"id": "6186779d",
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.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"
]
}
]
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"id": "13abf542",
"outputId": "42a52c37-29f9-4f43-a70f-c285886f0c10"
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"source": [
"trainer = Trainer(\n",
" model=model, # the instantiated 🤗 Transformers model to be trained\n",
" args=training_args, # training arguments, defined above\n",
" train_dataset=train_dataset, # training dataset\n",
" eval_dataset=val_dataset # evaluation dataset\n",
")\n",
"trainer.train()"
],
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"outputs": [
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"text": [
"***** Running training *****\n",
" Num examples = 5293\n",
" Num Epochs = 50\n",
" Instantaneous batch size per device = 64\n",
" Total train batch size (w. parallel, distributed & accumulation) = 64\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 4150\n"
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" <thead>\n",
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" <th>Step</th>\n",
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"text": [
"***** Running Evaluation *****\n",
" Num examples = 279\n",
" Batch size = 64\n",
"Saving model checkpoint to ./results/checkpoint-500\n",
"Configuration saved in ./results/checkpoint-500/config.json\n",
"Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
"***** Running Evaluation *****\n",
" Num examples = 279\n",
" Batch size = 64\n",
"Saving model checkpoint to ./results/checkpoint-1000\n",
"Configuration saved in ./results/checkpoint-1000/config.json\n",
"Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n",
"***** Running Evaluation *****\n",
" Num examples = 279\n",
" Batch size = 64\n",
"Saving model checkpoint to ./results/checkpoint-1500\n",
"Configuration saved in ./results/checkpoint-1500/config.json\n",
"Model weights saved in ./results/checkpoint-1500/pytorch_model.bin\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lsoxR09ymdHv"
},
"source": [
""
],
"id": "lsoxR09ymdHv",
"execution_count": null,
"outputs": []
}
]
}
