README.md

---
datasets:
- xap/everest-ner
language:
- ne
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: token-classification
model_type: xlm-roberta-base
base_model: xlm-roberta-base
tags:
- ner
- Nepali
---

## Model Overview
This model is a Named Entity Recognition (NER) model fine-tuned on the Everest NER dataset, which is a dataset for recognizing named entities in Nepali text. The base model used for fine-tuning is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), a multilingual transformer model that supports over 100 languages.

### How to Use
You can use this model with the Hugging Face `transformers` library as follows:

```python
from transformers import pipeline

# Load the NER pipeline
ner = pipeline("ner", model="bishaldpande/Ner-xlm-roberta-base")

# Example input
text = "सगरमाथा विश्वको अग्लो हिमाल हो।"

# Perform NER
entities = ner(text)
print(entities)
```

## Cite our work:

```bib
@InProceedings{10.1007/978-3-031-36822-6_8,
author="Pande, Bishal Debb
and Shakya, Aman
and Panday, Sanjeeb Prasad
and Joshi, Basanta",
editor="Fujita, Hamido
and Wang, Yinglin
and Xiao, Yanghua
and Moonis, Ali",
title="Named Entity Recognition for Nepali Using BERT Based Models",
booktitle="Advances and Trends in Artificial Intelligence. Theory and Applications",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="93--104",
isbn="978-3-031-36822-6"
}
```