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ShivamSharma8168/
hindi-sentiment-model

Name: hindi-sentiment-model
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🇮🇳 Hindi Sentiment Analysis Model

This repository contains a fine-tuned Hindi Sentiment Analysis model built using the HuggingFace Transformers library. It predicts sentiment for Hindi text and classifies it into:

Positive
Neutral
Negative

This model is suitable for Hindi tweets, reviews, comments, and general Hindi language sentiment tasks.

🚀 Model Overview

Model type: Sequence Classification
Base model: Multilingual Transformer
Framework: HuggingFace Transformers
Language: Hindi (hi)
Task: Sentiment Classification

This model was fine-tuned using a labeled Hindi dataset containing user-generated text such as tweets/comments.

🧠 Intended Usage

You can use this model for:

Hindi sentiment analysis
Social media monitoring
Review classification
Feedback analysis
Tweet polarity detection

🔍 Example Inference (Python)

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "ShivamSharma8168/hindi-sentiment-model"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

labels = ["negative", "neutral", "positive"]

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    pred = torch.argmax(outputs.logits, dim=1).item()
    return labels[pred]

print(predict_sentiment("यह फ़िल्म बहुत अच्छी है"))   # positive
print(predict_sentiment("मुझे यह पसंद नहीं आया"))      # negative