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--- |
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base_model: |
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- dslim/bert-base-NER |
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pipeline_tag: token-classification |
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tags: |
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- token-classification |
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- pytorch |
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- transformers |
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- named-entity-recognition |
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metrics: |
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- seqeval |
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--- |
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# bert-base-mountain-NER |
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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. |
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It is ideal for applications that involve extracting geographic information from travel literature, research documents, or any content related to natural landscapes. |
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## Dataset |
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The model was trained using approximately 115 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. |
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## How to Use |
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You can easily integrate this model with the Transformers library's NER pipeline: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# Load model and tokenizer |
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model_name = "Lizrek/bert-base-mountain-NER" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForTokenClassification.from_pretrained(model_name) |
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# Create a pipeline for NER |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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# Example usage |
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example = "Mount Fuji in Japan are example of volcanic mountain.." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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## Example Output |
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For the above input, the model provides the following output: |
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```python |
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[{'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}] |
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``` |
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This output highlights recognized mountain names, providing metadata such as entity type, confidence score, and word position. |
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## Limitations |
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- The model is specialized for mountain names and may not be effective in recognizing other types of geographical entities such as rivers or lakes. |
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- If the input text is significantly different from the training data in style or terminology, accuracy may be affected. |
