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| library_name: transformers |
| tags: [] |
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| # Model Card for Model ID |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. |
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| The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities. |
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| Features: |
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| Fine-tuned on a custom dataset that includes sentences both with and without mountain names. |
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| Uses focal loss to handle class imbalance, which ensures the model focuses on correctly classifying rare mountain names. |
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| Token-level classification for identifying the B-MOUNTAIN, I-MOUNTAIN, and O (non-entity) labels. |
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| Balances training between sentences with mountains (80%) and without mountains (20%). |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| This is the model card of a 🤗 transformers model that has been pushed on the Hub. |
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| - **Developed by:** Oleksandr Kharytonov |
| - **Model type:** BERT |
| - **Language(s) (NLP):** Python |
| - **License:** MIT |
| - **Finetuned from model [optional]:** https://huggingface.co/dslim/bert-large-NER |
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| ### Model Sources |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** https://github.com/Shah1st/mountain-ner |
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| ### Direct Use |
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| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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| ```python |
| from transformers import AutoTokenizer, AutoModelForTokenClassification |
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| tokenizer = AutoTokenizer.from_pretrained('./saved_model') |
| model = AutoModelForTokenClassification.from_pretrained('./saved_model') |
| ``` |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. |
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| ## How to Get Started with the Model |
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| Use the github below to get started with the model. |
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| https://github.com/Shah1st/mountain-ner |
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| ## Training Details |
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| ### Training Data |
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| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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| "DFKI-SLT/few-nerd", "supervised" |
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| Filter for sentences with 'fine_ner_tags' == 24 (mountains) |
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| ## Evaluation |
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| <!-- This section describes the evaluation protocols and provides the results. --> |
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| 'eval_loss': 0.009154710918664932, 'eval_macro_f1': 0.8952192988290304, 'eval_accuracy': 0.9746226793108054 |
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| ### Testing Data, Factors & Metrics |
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| #### Metrics |
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| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| macro F1: 0.895 |
| Accuracy: 0.974 |
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| #### Summary |
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| This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities. |
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