language:
- en
base_model:
- google-bert/bert-large-uncased
pipeline_tag: token-classification
Model Card for Mountain NER Model
Model Summary
This model is a fine-tuned Named Entity Recognition (NER) model specifically designed to identify mountain names in text. It is trained to detect and classify mountain entities using labeled data and state-of-the-art NER architectures. The model can handle both single-word and multi-word mountain names (e.g., "Kilimanjaro" or "Rocky Mountains").
Intended Use
Task: Named Entity Recognition (NER) for mountain name identification.
Input: A text string containing sentences or paragraphs.
Output: A list of tokens annotated with labels:
B-MOUNTAIN: Beginning of a mountain name.
I-MOUNTAIN: Inside a mountain name.
O: Outside of any mountain entity.
How to Use
You can load this model using the Hugging Face transformers library:
from transformers import BertTokenizer, BertForTokenClassification
import torch
tokenizer = BertTokenizer.from_pretrained("your_username/your_model")
model = BertForTokenClassification.from_pretrained("your_username/your_model")
text = "The Kilimanjaro is one of the most famous mountains."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"].squeeze())
labels = [model.config.id2label[label] for label in predictions.squeeze().tolist()]
print(list(zip(tokens, labels)))
Dataset
The dataset includes annotated examples of text with mountain names in BIO format:
- Training Set: 350 examples
- Validation Set: 75 examples
- Test Set: 75 examples
The dataset was created by combining known mountain names with sentences containing them.
Limitations
The model is specifically designed for mountain names and may not generalize to other named entities.
Performance may degrade on noisy or informal text.
Multi-word mountain names must be tokenized correctly for proper recognition.
Repository: [https://github.com/Yevheniia-Ilchenko/Bert_NER]
Training Details
The model was fine-tuned using the BERT Base Uncased architecture for token classification. Below are the training details:
- Model Architecture: BERT for Token Classification (
bert-base-uncased). - Dataset: Custom-labeled dataset in BIO format for mountain name recognition.
- Hyperparameters:
- Learning Rate:
2e-4 - Batch Size:
16 - Maximum Sequence Length:
128 - Number of Epochs:
3
- Learning Rate:
- Optimizer: AdamW
- Warmup Steps:
500 - Weight Decay:
0.01 - Evaluation Strategy: Steps-based evaluation with automatic saving of the best model.
- Training Arguments:
save_total_limit=3: Limits the number of saved checkpoints.load_best_model_at_end=True: Ensures the best model is used after training.
- Training Performance:
- Training Runtime:
570.44 seconds - Training Samples per Second:
1.841 - Training Steps per Second:
0.116 - Final Training Loss:
0.4017
- Training Runtime:
- Evaluation Metrics:
- Evaluation Loss:
0.0839 - Precision:
97.11% - Recall:
96.89% - F1 Score:
96.91% - Evaluation Runtime:
13.76 seconds - Samples per Second:
5.449 - Steps per Second:
0.726
- Evaluation Loss: