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--- |
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library_name: transformers |
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base_model: huawei-noah/TinyBERT_General_4L_312D |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: TinyBERT-finetuned-NER |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8465303458777463 |
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- name: Recall |
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type: recall |
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value: 0.870679046873252 |
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- name: F1 |
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type: f1 |
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value: 0.8584348977003253 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9670516466233497 |
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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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# TinyBERT-finetuned-NER |
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This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1232 |
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- Precision: 0.8465 |
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- Recall: 0.8707 |
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- F1: 0.8584 |
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- Accuracy: 0.9671 |
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.5173 | 1.0 | 878 | 0.2116 | 0.7429 | 0.7756 | 0.7589 | 0.9493 | |
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| 0.196 | 2.0 | 1756 | 0.1528 | 0.8262 | 0.8383 | 0.8323 | 0.9620 | |
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| 0.1444 | 3.0 | 2634 | 0.1355 | 0.8447 | 0.8606 | 0.8526 | 0.9652 | |
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| 0.116 | 4.0 | 3512 | 0.1255 | 0.8452 | 0.8660 | 0.8555 | 0.9663 | |
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| 0.1116 | 5.0 | 4390 | 0.1232 | 0.8465 | 0.8707 | 0.8584 | 0.9671 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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