README.md

metadata

language: ne
license: apache-2.0
tags:
  - sentiment-analysis
  - nepali
  - onnx
  - bert
  - text-classification
datasets:
  - custom-nepali-sentiment
metrics:
  - f1
  - accuracy
model-index:
  - name: mohit4519/nepali-sentiment
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: Nepali Sentiment Dataset
          type: custom
        metrics:
          - type: f1
            value: 0.73
            name: Macro F1

Nepali Sentiment Analysis (ONNX)

This model is a fine-tuned BERT model for Nepali sentiment analysis, exported to ONNX format for optimized inference.

Model Details

• Base Model: Shushant/nepaliBERT
• Task: Sentiment Classification (3-class)
• Labels:
  • 0: Negative
  • 1: Positive
  • 2: Neutral
• Format: ONNX (optimized for fast inference)

Usage

Installation

pip install transformers optimum[onnxruntime]

Inference

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
import torch

# Load model and tokenizer
model = ORTModelForSequenceClassification.from_pretrained("mohit4519/nepali-sentiment")
tokenizer = AutoTokenizer.from_pretrained("Shushant/nepaliBERT")

# Predict sentiment
text = "यो धेरै राम्रो छ"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=-1).item()

sentiment_map = {0: 'Negative', 1: 'Positive', 2: 'Neutral'}
print(f"Sentiment: {sentiment_map[prediction]}")

Performance on test set

• Macro F1 Score: 0.73
• Accuracy: 0.76

Training Data

Trained on Nepali sentiment dataset containing social media text, reviews, and comments.

Limitations

• Best performance on Nepali text
• May have reduced accuracy on code-mixed or transliterated text
• Performance varies across different domains