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hetbhalani/
roberta-enhanced-for-sentiment

Name: roberta-enhanced-for-sentiment
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🎬 Movie Review Sentiment Analysis — Fine-tuned RoBERTa

🧠 Model Overview

This model is a fine-tuned version of RoBERTa-base for Movie Review Sentiment Analysis.
It classifies text into three categories: Positive, Negative, and Neutral.

Attribute	Description
Model Type	Transformer-based (RoBERTa-base)
Task	Sentiment Classification
Labels	Positive, Negative, Neutral
Framework	PyTorch / Hugging Face Transformers
Language	English

📦 Intended Use

Use Cases

Movie review sentiment prediction
Movie insights dashboards
Recommendation systems
NLP learning & research
User feedback analysis

Not Intended For

Hate speech or legal/medical decision making
Languages other than English
Sensitive / life-critical decision making

🧠 Training Details

Component	Details
Base Model	`roberta-base`
Dataset	Custom dataset of movie reviews
Split	80% train / 10% validation / 10% test
Loss	Cross-Entropy
Optimizer	AdamW
Epochs	3–6
Max Sequence Length	256
Batch Size	16
Learning Rate	2e-5

📊 Evaluation Metrics

Metric	Score
Accuracy	add value
F1 Score	add value
Precision	add value
Recall	add value

Replace placeholders with your actual validation results.

🚀 How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("hetbhalani/roberta-enhanced-for-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("hetbhalani/roberta-enhanced-for-sentiment")

text = "I really loved this movie, amazing acting and direction!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = torch.softmax(outputs.logits, dim=1)
print(pred)