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# Step 1: Install Required Libraries |
# Uncomment the following lines if you are running this in a new environment |
# !pip install transformers torch datasets pandas |
# Step 2: Import Required Libraries |
import pandas as pd |
import torch |
from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments |
from datasets import load_dataset |
# Step 3: Prepare Your Dataset |
# Create a sample dataset (replace this with your actual dataset) |
data = { |
'text': [ |
"అయ్యో, నిన్ను చూసి చాలా కాలమైంది!", # 0 |
"నువ్వు ఈ రోజు రాంబాబు ఇంటికి వెళ్ళావా?", # 1 |
"హే, నువ్వు ఈ కొత్త సినిమా చూసావా?", # 0 |
"ఈ ప్రాజెక్ట్ మీద పని ఎలా జరుగుతోంది?", # 1 |
"సూపర్ మాస్! బాగా ఎంజాయ్ చేశా.", # 0 |
"చాలా కష్టంగా ఉంది, కానీ మామూలుగా ఉంది." # 1 |
], |
'label': [0, 1, 0, 1, 0, 1] # Example labels |
} |
# Create a DataFrame |
df = pd.DataFrame(data) |
# Split into train and test sets |
train_texts = df['text'][:4].tolist() |
train_labels = df['label'][:4].tolist() |
test_texts = df['text'][4:].tolist() |
test_labels = df['label'][4:].tolist() |
# Step 4: Tokenization |
tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased') # Use a multilingual model |
train_encodings = tokenizer(train_texts, truncation=True, padding=True) |
test_encodings = tokenizer(test_texts, truncation=True, padding=True) |
# Step 5: Create a Dataset Class |
class ColloquialDataset(torch.utils.data.Dataset): |
def _init_(self, encodings, labels): |
self.encodings = encodings |
self.labels = labels |
def _getitem_(self, idx): |
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} |
item['labels'] = torch.tensor(self.labels[idx]) |
return item |
def _len_(self): |
return len(self.labels) |
# Create dataset objects |
train_dataset = ColloquialDataset(train_encodings, train_labels) |
test_dataset = ColloquialDataset(test_encodings, test_labels) |
# Step 6: Load the Model |
model = BertForSequenceClassification.from_pretrained('bert-base-multilingual-cased', num_labels=2) |
# Step 7: Set Up Training Arguments |
training_args = TrainingArguments( |
output_dir='./results', # output directory |
num_train_epochs=3, # total number of training epochs |
per_device_train_batch_size=2, # batch size per device during training |
per_device_eval_batch_size=2, # batch size for evaluation |
warmup_steps=10, # number of warmup steps for learning rate scheduler |
weight_decay=0.01, # strength of weight decay |
logging_dir='./logs', # directory for storing logs |
logging_steps=10, |
) |
# Step 8: Initialize the Trainer |
trainer = Trainer( |
model=model, |
args=training_args, |
train_dataset=train_dataset, |
eval_dataset=test_dataset |
) |
# Step 9: Train the Model |
trainer.train() |
# Step 10: Evaluate the Model |
eval_results = trainer.evaluate() |
print("Evaluation Results:", eval_results) |
# Step 11: Save the Fine-Tuned Model |
model.save_pretrained('./fine_tuned_model') |
tokenizer.save_pretrained('./fine_tuned_model') |
print("Model and tokenizer saved successfully!") |
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