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{
"cells": [
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
"Epoch 1/3: 100%|ββββββββββ| 1293/1293 [35:14<00:00, 1.64s/batch, Loss=0.000204]\n",
"Epoch 2/3: 100%|ββββββββββ| 1293/1293 [35:04<00:00, 1.63s/batch, Loss=0.000154]\n",
"Epoch 3/3: 100%|ββββββββββ| 1293/1293 [34:39<00:00, 1.61s/batch, Loss=7.12e-5] \n"
]
}
],
"source": [
"import torch\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from transformers import AutoTokenizer, AutoModelForMaskedLM, AdamW\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"\n",
"class GrammarCorrectionDataset(Dataset):\n",
" def __init__(self, sentences, corrected_sentences, tokenizer, max_length=128):\n",
" self.sentences = sentences\n",
" self.corrected_sentences = corrected_sentences\n",
" self.tokenizer = tokenizer\n",
" self.max_length = max_length\n",
"\n",
" def __len__(self):\n",
" return len(self.sentences)\n",
" \n",
" def __getitem__(self, idx):\n",
" input_sentence = self.sentences[idx]\n",
" corrected_sentence = self.corrected_sentences[idx]\n",
"\n",
" # Tokenize the input and corrected sentences separately\n",
" inputs = self.tokenizer(\n",
" [input_sentence], # Pass as a list\n",
" [corrected_sentence], # Pass as a list\n",
" return_tensors=\"pt\",\n",
" padding=\"max_length\",\n",
" truncation=True,\n",
" max_length=self.max_length\n",
" )\n",
"\n",
" return {\n",
" \"input_ids\": inputs[\"input_ids\"].flatten(),\n",
" \"attention_mask\": inputs[\"attention_mask\"].flatten(),\n",
" \"labels\": inputs[\"input_ids\"].flatten() # Use input_ids as labels for MLM\n",
" }\n",
"\n",
"\n",
"\n",
"def pad_collate(batch):\n",
" # Find the length of the longest sentence in the batch\n",
" max_len = max(len(batch_item[\"input_ids\"]) for batch_item in batch)\n",
" \n",
" # Pad each input to the length of the longest sentence in the batch\n",
" for batch_item in batch:\n",
" input_ids = batch_item[\"input_ids\"]\n",
" attention_mask = batch_item[\"attention_mask\"]\n",
" labels = batch_item[\"labels\"]\n",
" \n",
" padded_input_ids = torch.nn.functional.pad(input_ids, (0, max_len - len(input_ids)), value=tokenizer.pad_token_id)\n",
" padded_attention_mask = torch.nn.functional.pad(attention_mask, (0, max_len - len(attention_mask)), value=0)\n",
" padded_labels = torch.nn.functional.pad(labels, (0, max_len - len(labels)), value=tokenizer.pad_token_id)\n",
" \n",
" batch_item[\"input_ids\"] = padded_input_ids\n",
" batch_item[\"attention_mask\"] = padded_attention_mask\n",
" batch_item[\"labels\"] = padded_labels\n",
" \n",
" return {\n",
" \"input_ids\": torch.stack([batch_item[\"input_ids\"] for batch_item in batch]),\n",
" \"attention_mask\": torch.stack([batch_item[\"attention_mask\"] for batch_item in batch]),\n",
" \"labels\": torch.stack([batch_item[\"labels\"] for batch_item in batch])\n",
" }\n",
"\n",
"\n",
"data = pd.read_csv(r'D:\\Thesis\\test_bert_data.csv')\n",
"data = data.dropna()\n",
"sentences = [str(i) for i in data['wrong'].values] # Convert Series to list\n",
"corrected_sentences = [str(i) for i in data['right1'].values]\n",
"\n",
"# Initialize tokenizer and model\n",
"tokenizer = AutoTokenizer.from_pretrained(\"jcblaise/roberta-tagalog-base\")\n",
"model = AutoModelForMaskedLM.from_pretrained(\"jcblaise/roberta-tagalog-base\")\n",
"\n",
"# Create dataset and dataloader\n",
"dataset = GrammarCorrectionDataset(sentences, corrected_sentences, tokenizer)\n",
"dataloader = DataLoader(dataset, batch_size=4, shuffle=True, collate_fn=pad_collate)\n",
"\n",
"\n",
"# Define optimizer and loss function\n",
"optimizer = AdamW(model.parameters(), lr=5e-5)\n",
"\n",
"num_epochs = 3\n",
"\n",
"model.train()\n",
"for epoch in range(num_epochs):\n",
" tqdm_dataloader = tqdm(dataloader, desc=f\"Epoch {epoch + 1}/{num_epochs}\", unit=\"batch\")\n",
" for batch in tqdm_dataloader:\n",
" input_ids = batch[\"input_ids\"]\n",
" attention_mask = batch[\"attention_mask\"]\n",
" labels = batch[\"labels\"]\n",
"\n",
" outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
" loss = outputs.loss\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # Update tqdm progress bar description with current loss\n",
" tqdm_dataloader.set_postfix({\"Loss\": loss.item()})\n",
"\n",
" # Close the tqdm progress bar for the epoch\n",
" tqdm_dataloader.close()\n",
"\n",
"# Save the fine-tuned model\n",
"#model.save_pretrained(\"fine_tuned_model\")\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"model.save_pretrained(\"fine_tuned_model\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original: Takbo <mask> takbo ang mga bata sa labas\n",
"Corrected: <s>Takbo ng takbo ang mga bata sa labas</s>\n",
"\n"
]
}
],
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
"\n",
"# Load the fine-tuned model\n",
"model = AutoModelForMaskedLM.from_pretrained(\"fine_tuned_model\")\n",
"\n",
"# Initialize tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"fine_tuned_model\")\n",
"\n",
"# Example new data\n",
"new_data = [\n",
" \"Takbo <mask> takbo ang mga bata sa labas\"\n",
"]\n",
"\n",
"# Tokenize the new data\n",
"tokenized_data = tokenizer(new_data, return_tensors=\"pt\", padding=True, truncation=True)\n",
"\n",
"# Pass the tokenized data through the model to get predictions\n",
"with torch.no_grad():\n",
" outputs = model(**tokenized_data)\n",
"\n",
"# Decode the predicted token IDs back to text\n",
"predicted_texts = tokenizer.batch_decode(outputs.logits.argmax(dim=-1))\n",
"\n",
"# Print the original sentences and their corrected versions\n",
"for original, corrected in zip(new_data, predicted_texts):\n",
" print(f\"Original: {original}\")\n",
" print(f\"Corrected: {corrected}\")\n",
" print()\n"
]
}
],
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