Gepe55o/mountain-ner-bert-base

Model Description

mountain-ner-bert-base is a fine-tuned model based on the BERT base architecture for mountain names Entity Recognition tasks. The model is trained on the merging of two datasets: NERetrieve, Few-NERD, Mountain-ner-dataset. The model is trained to recognize two types of entities: LABEL_0 (other), LABEL_1 (mountain names).

• Model Architecture: BERT base
• Task: mountain names entity recognition
• Training Data: mountain-ner-dataset

Performance

Metrics:

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.027400	0.030793	0.988144	0.815692	0.924621	0.866748
2	0.020600	0.024568	0.991119	0.872988	0.921036	0.896369
3	0.012900	0.024072	0.991923	0.889878	0.920171	0.904771

Best model performance achieved at epoch 3 with:

• F1 Score: 0.9048
• Accuracy: 0.9919
• Precision: 0.8899
• Recall: 0.9202

How to use

from transformers import AutoModel, AutoTokenizer, pipeline

model = AutoModel.from_pretrained("Gepe55o/mountain-ner-bert-base")
tokenizer = AutoTokenizer.from_pretrained("Gepe55o/mountain-ner-bert-base")

text = "Mount Everest is the highest mountain in the world."

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
result = nlp(text)