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---
datasets:
- DFKI-SLT/few-nerd
language:
- en
metrics:
- f1=0.87
- precision
- recall
---
# HuggingsaurusRex/bert-base-uncased-for-mountain-ner
## Purpose
Detect mountain names in text using token classification.
## Usage
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
# Load model and tokenizer
model = AutoModelForTokenClassification.from_pretrained('huggingsaurusRex/bert-base-uncased-for-mountain-ner')
tokenizer = AutoTokenizer.from_pretrained('huggingsaurusRex/bert-base-uncased-for-mountain-ner')
# Create NER pipeline
ner = pipeline('ner', model=model, tokenizer=tokenizer)
# Perform inference
res = ner("I spent days climbing the Mount Everest.")
print(res)
```
## Architecture
The model is a BERT-based token classification model fine-tuned on the Few-NERD dataset.
## Results
- F1-Score: 0.87
- Precision: 0.84
- Recall: 0.91
## Direct Link
[HuggingsaurusRex/bert-base-uncased-for-mountain-ner](https://huggingface.co/huggingsaurusRex/bert-base-uncased-for-mountain-ner)
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