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--- |
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license: mit |
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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: deberta-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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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9577488309953239 |
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- name: Recall |
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type: recall |
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value: 0.9651632446987546 |
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- name: F1 |
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type: f1 |
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value: 0.961441743503772 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9907182964622135 |
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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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# deberta-finetuned-ner |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0515 |
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- Precision: 0.9577 |
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- Recall: 0.9652 |
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- F1: 0.9614 |
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- Accuracy: 0.9907 |
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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: 8 |
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- eval_batch_size: 8 |
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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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| 0.0742 | 1.0 | 1756 | 0.0526 | 0.9390 | 0.9510 | 0.9450 | 0.9868 | |
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| 0.0374 | 2.0 | 3512 | 0.0528 | 0.9421 | 0.9554 | 0.9487 | 0.9879 | |
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| 0.0205 | 3.0 | 5268 | 0.0505 | 0.9505 | 0.9636 | 0.9570 | 0.9900 | |
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| 0.0089 | 4.0 | 7024 | 0.0528 | 0.9531 | 0.9636 | 0.9583 | 0.9898 | |
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| 0.0076 | 5.0 | 8780 | 0.0515 | 0.9577 | 0.9652 | 0.9614 | 0.9907 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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