Sentiment Analysis

🚀 Sentiment Analysis Model using DistilBERT

This repository contains a sentiment analysis model fine - tuned on the IMDb movie reviews dataset with DistilBERT architecture. It can classify text inputs into positive or negative sentiment categories.

🚀 Quick Start

To use the model, you need to install the transformers library from Hugging Face. You can install it using the following command:

pip install transformers

Once installed, you can use the following code to classify text with this model:

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch

# Load the tokenizer and model from the Hugging Face Hub
tokenizer = DistilBertTokenizer.from_pretrained(Pranav-10/Sentimental_Analysis)
model = DistilBertForSequenceClassification.from_pretrained(Pranav-10/Sentimental_Analysis)

# Example text
text = "I loved this movie. The performances were fantastic!"

# Tokenize text and convert to tensor
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

# Predict sentiment
with torch.no_grad():
    logits = model(**inputs).logits

# Convert logits to probabilities using softmax
probabilities = torch.softmax(logits, dim=-1)

# Output the result
print(probabilities)

✨ Features

• Based on DistilBERT: The model is built on the DistilBERT architecture, which is a smaller, faster, cheaper, and lighter version of BERT. It retains most of BERT's performance while being more efficient, making it ideal for sentiment analysis tasks where size and speed are crucial.
• Fine - tuned on IMDb: The model has been fine - tuned on the IMDb dataset, which contains 50,000 movie reviews labeled as positive or negative.

📚 Documentation

Model Description

The model is based on the DistilBERT architecture. DistilBERT is a more efficient alternative to BERT, retaining most of its performance. It has been fine - tuned on the IMDb dataset, which consists of 50,000 movie reviews labeled as positive or negative. This makes it well - suited for sentiment analysis tasks.

Evaluation Results

The model achieved the following performance on the IMDb dataset:

Training Procedure

The model was trained using the following steps:

• Pre - processing: The dataset was pre - processed by converting all reviews to lowercase and tokenizing using the DistilBERT tokenizer.
• Optimization: The Adam optimizer was used with a learning rate of 2e - 5, a batch size of 16, and the model was trained for 3 epochs.
• Hardware: Training was performed on a single NVIDIA GTX 1650 GPU.

📄 License

This project is licensed under the Apache 2.0 license.