---
library_name: transformers
license: apache-2.0
datasets:
- sanjeev-bhandari01/nepali-summarization-dataset
base_model:
- facebook/mbart-large-cc25
---
# Nepali Article Title Generator

This is a fine-tuned MBart model for generating titles (summaries) for Nepali articles. The model was fine-tuned on a Nepali summarization dataset and is designed to generate concise and relevant titles for given Nepali text.

## Model Details

- **Model Type**: MBart (Multilingual BART)
- **Fine-Tuned For**: Nepali Article Title Generation
- **Languages**: Nepali (`ne_NP`)
- **Model Size**: Large
- **Training Dataset**: Nepali Summarization Dataset (first 50,000 samples)
- **Fine-Tuning Framework**: Hugging Face Transformers
- **Fine-Tuning Epochs**: 3
- **Max Input Length**: 512 tokens
- **Max Target Length**: 128 tokens

## How to Use

You can use this model to generate titles for Nepali articles. Below is an example of how to load and use the model.

### Installation

First, install the required libraries:

```bash
pip install torch transformers
```

```bash
import torch
from transformers import MBartTokenizer, MBartForConditionalGeneration

# Load the fine-tuned model
MODEL_PATH = "Dragneel/nepali-article-title-generator" 
model = MBartForConditionalGeneration.from_pretrained(MODEL_PATH)
tokenizer = MBartTokenizer.from_pretrained(MODEL_PATH)

# Set the source and target language
tokenizer.src_lang = "ne_NP"
tokenizer.tgt_lang = "ne_NP"

# Define the input text
input_text = """
नेपालको पर्यटन उद्योगमा कोरोनाको प्रभावले गर्दा ठूलो मन्दी आएको छ। 
विश्वभर यात्रा प्रतिबन्ध लागू भएपछि नेपाल आउने पर्यटकको संख्या न्यून भएको छ। 
यसले गर्दा होटल, यातायात र अन्य पर्यटन सम्बन्धी व्यवसायमा ठूलो असर परेको छ। 
सरकारले पर्यटन उद्योगलाई बचाउन विभिन्न उपायहरू ल्याएको छ, तर अहिलेसम्म कुनै ठूलो सुधार देखिएको छैन।
"""

# Tokenize the input
inputs = tokenizer(
    input_text,
    return_tensors="pt",
    max_length=512,
    truncation=True,
    padding="max_length"
)

# Generate summary
model.eval()  # Set the model to evaluation mode
with torch.no_grad():
    output_ids = model.generate(
        inputs["input_ids"],
        max_length=128,  # Set the maximum length of the generated text
        num_beams=4,  # Beam search for better quality
        early_stopping=True
    )

# Decode the generated text
summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print("Generated Title:", summary)
```