File size: 1,994 Bytes
6a2c993 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
import torch
# Load dataset
dataset = load_dataset('glue', 'mrpc')
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# Tokenize dataset
def tokenize_function(examples):
return tokenizer(examples['sentence1'], examples['sentence2'], truncation=True, padding='max_length', max_length=128)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Set up Trainer
training_args = TrainingArguments(
output_dir='./results',
evaluation_strategy='epoch',
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets['train'],
eval_dataset=tokenized_datasets['validation'],
)
# Train the model
trainer.train()
# Save the model
model.save_pretrained('./my_model')
tokenizer.save_pretrained('./my_model')
# Load the trained model
model = AutoModelForSequenceClassification.from_pretrained('./my_model')
tokenizer = AutoTokenizer.from_pretrained('./my_model')
# Function to predict sentiment
def predict(text1, text2):
inputs = tokenizer(text1, text2, return_tensors='pt', truncation=True, padding='max_length', max_length=128)
outputs = model(**inputs)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1).item()
return "Similar" if predictions == 0 else "Not Similar"
# Web interface
if __name__ == "__main__":
import gradio as gr
iface = gr.Interface(
fn=predict,
inputs=["text", "text"],
outputs="text",
title="MRPC Sentiment Analysis",
description="Determine if two sentences are semantically equivalent."
)
iface.launch() |