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--- |
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language: en |
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license: mit |
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base_model: answerdotai/ModernBERT-base |
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tags: |
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- token-classification |
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- ModernBERT-base |
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datasets: |
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- disham993/ElectricalNER |
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metrics: |
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- epoch: 1.0 |
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- eval_precision: 0.8935291782453354 |
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- eval_recall: 0.9075806451612904 |
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- eval_f1: 0.9005001000200039 |
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- eval_accuracy: 0.9586046624222324 |
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- eval_runtime: 2.509 |
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- eval_samples_per_second: 601.44 |
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- eval_steps_per_second: 9.566 |
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--- |
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# disham993/electrical-ner-modernbert-base |
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## Model description |
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This model is fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) for token-classification tasks. |
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## Training Data |
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The model was trained on the disham993/ElectricalNER dataset. |
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## Model Details |
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- **Base Model:** answerdotai/ModernBERT-base |
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- **Task:** token-classification |
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- **Language:** en |
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- **Dataset:** disham993/ElectricalNER |
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## Training procedure |
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### Training hyperparameters |
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[Please add your training hyperparameters here] |
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## Evaluation results |
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### Metrics\n- epoch: 1.0\n- eval_precision: 0.8935291782453354\n- eval_recall: 0.9075806451612904\n- eval_f1: 0.9005001000200039\n- eval_accuracy: 0.9586046624222324\n- eval_runtime: 2.509\n- eval_samples_per_second: 601.44\n- eval_steps_per_second: 9.566 |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-ner-modernbert-base") |
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model = AutoModel.from_pretrained("disham993/electrical-ner-modernbert-base") |
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``` |
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## Limitations and bias |
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[Add any known limitations or biases of the model] |
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## Training Infrastructure |
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[Add details about training infrastructure used] |
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## Last update |
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2024-12-30 |
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