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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: bert-to-distilbert-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: 0.014488935721812434 |
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- name: Recall |
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type: recall |
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value: 0.018512285425782565 |
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- name: F1 |
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type: f1 |
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value: 0.016255356878971478 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7597280273150055 |
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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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# bert-to-distilbert-NER |
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 44.0386 |
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- Precision: 0.0145 |
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- Recall: 0.0185 |
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- F1: 0.0163 |
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- Accuracy: 0.7597 |
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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: 6e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 33 |
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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: 15 |
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- mixed_precision_training: Native AMP |
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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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| 201.4012 | 1.0 | 110 | 133.7231 | 0.0153 | 0.0106 | 0.0125 | 0.7539 | |
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| 106.9317 | 2.0 | 220 | 99.3629 | 0.0266 | 0.0305 | 0.0284 | 0.7593 | |
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| 81.3601 | 3.0 | 330 | 80.3763 | 0.0159 | 0.0214 | 0.0183 | 0.7604 | |
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| 63.8325 | 4.0 | 440 | 67.7620 | 0.0179 | 0.0244 | 0.0207 | 0.7599 | |
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| 52.0271 | 5.0 | 550 | 59.0806 | 0.0203 | 0.0268 | 0.0231 | 0.7598 | |
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| 44.4419 | 6.0 | 660 | 55.3208 | 0.0211 | 0.0278 | 0.0240 | 0.7603 | |
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| 39.2351 | 7.0 | 770 | 52.4510 | 0.0170 | 0.0222 | 0.0193 | 0.7598 | |
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| 35.3438 | 8.0 | 880 | 50.4576 | 0.0205 | 0.0268 | 0.0232 | 0.7604 | |
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| 32.7385 | 9.0 | 990 | 48.3418 | 0.0173 | 0.0227 | 0.0197 | 0.7595 | |
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| 30.6531 | 10.0 | 1100 | 46.7304 | 0.0147 | 0.0188 | 0.0165 | 0.7600 | |
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| 29.0811 | 11.0 | 1210 | 46.3386 | 0.0151 | 0.0190 | 0.0168 | 0.7599 | |
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| 27.9501 | 12.0 | 1320 | 45.4516 | 0.0163 | 0.0204 | 0.0181 | 0.7604 | |
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| 26.7452 | 13.0 | 1430 | 44.3425 | 0.0154 | 0.0199 | 0.0173 | 0.7592 | |
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| 25.5367 | 14.0 | 1540 | 44.0415 | 0.0146 | 0.0190 | 0.0165 | 0.7594 | |
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| 24.5507 | 15.0 | 1650 | 44.0386 | 0.0145 | 0.0185 | 0.0163 | 0.7597 | |
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### Framework versions |
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- Transformers 4.19.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.1 |
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- Tokenizers 0.12.1 |
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