Shah1st/mountain-ner-model

Model Card for Model ID

This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text.

The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.

Features:

Fine-tuned on a custom dataset that includes sentences both with and without mountain names.

Uses focal loss to handle class imbalance, which ensures the model focuses on correctly classifying rare mountain names.

Token-level classification for identifying the B-MOUNTAIN, I-MOUNTAIN, and O (non-entity) labels.

Balances training between sentences with mountains (80%) and without mountains (20%).

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub.

• Developed by: Oleksandr Kharytonov
• Model type: BERT
• Language(s) (NLP): Python
• License: MIT
• Finetuned from model [optional]: https://huggingface.co/dslim/bert-large-NER

Model Sources

• Repository: https://github.com/Shah1st/mountain-ner

Direct Use

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained('./saved_model')
model = AutoModelForTokenClassification.from_pretrained('./saved_model')

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

How to Get Started with the Model

Use the github below to get started with the model.

https://github.com/Shah1st/mountain-ner

Training Details

Training Data

"DFKI-SLT/few-nerd", "supervised"

Filter for sentences with 'fine_ner_tags' == 24 (mountains)

Evaluation

'eval_loss': 0.009154710918664932, 'eval_macro_f1': 0.8952192988290304, 'eval_accuracy': 0.9746226793108054

Testing Data, Factors & Metrics

Metrics

macro F1: 0.895 Accuracy: 0.974

Summary

This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.