Create README.md

README.md

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+---
+tags:
+- token-classification
+- pytorch
+- transformers
+- named-entity-recognition
+widget:
+- text: Mount Fuji in Japan are example of volcanic mountain.
+pipeline_tag: token-classification
+metrics:
+- seqeval
+base_model:
+- dslim/bert-base-NER
+---
+
+# bert-base-mountain-NER
+
+This model is a specialized adaptation of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER), tailored for recognizing mountain names with a focus on geographical texts. Unlike the original, this model retains all 12 hidden layers and has been specifically fine-tuned to achieve high precision in identifying mountain-related entities across diverse texts.
+
+It is ideal for applications that involve extracting geographic information from travel literature, research documents, or any content related to natural landscapes.
+
+## Dataset
+
+The model was trained using approximately 150 samples generated specifically for mountain name recognition. These samples were created with the assistance of ChatGPT, focusing on realistic use cases for mountain-related content in the NER format.
+
+## How to Use
+
+You can easily integrate this model with the Transformers library's NER pipeline:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model and tokenizer
+model_name = "Lizrek/bert-base-mountain-NER"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+# Create a pipeline for NER
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+
+# Example usage
+example = "Mount Fuji in Japan are example of volcanic mountain.."
+ner_results = nlp(example)
+print(ner_results)
+```
+
+## Example Output
+
+For the above input, the model provides the following output:
+
+```python
+[{'entity': 'B-MOUNTAIN_NAME', 'score': np.float32(0.9827131), 'index': 1, 'word': 'Mount', 'start': 0, 'end': 5}, {'entity': 'I-MOUNTAIN_NAME', 'score': np.float32(0.98952174), 'index': 2, 'word': 'Fuji', 'start': 6, 'end': 10}]
+```
+
+This output highlights recognized mountain names, providing metadata such as entity type, confidence score, and word position.
+
+## Limitations
+
+- The model is specialized for mountain names and may not be effective in recognizing other types of geographical entities such as rivers or lakes.
+- If the input text is significantly different from the training data in style or terminology, accuracy may be affected.