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README.md

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 # Info
 
 NER model trained on everest ner dataset for Nepali NER.
+
+---
+language: ne
+tags:
+- token-classification
+- ner
+- xlm-roberta
+- nepali
+- everest-ner
+license: mit
+datasets:
+- everest-ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model_name: NER Model for Nepali (Everest NER)
+model_type: xlm-roberta-base
+base_model: xlm-roberta-base
+pipeline_tag: token-classification
+---
+
+# NER Model for Nepali (Everest NER)
+
+## 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.
+
+## Intended Use
+The model is designed to identify and classify named entities in Nepali text into categories such as Person, Organization, Location, and others. It is particularly useful for NLP applications involving the Nepali language, such as information extraction, text analysis, and content categorization.
+
+### 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="your-username/everest-ner-xlm-roberta-base")
+
+# Example input
+text = "सगरमाथा विश्वको अग्लो हिमाल हो।"
+
+# Perform NER
+entities = ner(text)
+print(entities)
+