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--- |
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library_name: transformers |
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license: mit |
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base_model: dslim/bert-base-NER |
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tags: |
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- ner |
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- bert |
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- token-classification |
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- generated_from_trainer |
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model-index: |
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- name: NER-BERT |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# NER-BERT |
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Token Accuracy: 1.0000 |
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- Token Precision: 1.0000 |
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- Token Recall: 1.0000 |
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- Token F1: 1.0000 |
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- Entity Precision: 0.9999 |
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- Entity Recall: 0.9999 |
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- Entity F1: 0.9999 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Token Accuracy | Token Precision | Token Recall | Token F1 | Entity Precision | Entity Recall | Entity F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------------:|:------------:|:--------:|:----------------:|:-------------:|:---------:| |
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| 0.0004 | 1.0 | 2250 | 0.0002 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9995 | 0.9996 | 0.9996 | |
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| 0.0003 | 2.0 | 4500 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9998 | 0.9999 | 0.9998 | |
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| 0.0001 | 3.0 | 6750 | 0.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9999 | 0.9999 | 0.9999 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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