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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lextreme |
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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: distilroberta-base-mapa_coarse-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: lextreme |
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type: lextreme |
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config: mapa_coarse |
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split: test |
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args: mapa_coarse |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7440758293838863 |
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- name: Recall |
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type: recall |
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value: 0.5805042016806723 |
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- name: F1 |
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type: f1 |
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value: 0.652190332326284 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9871584939520047 |
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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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# distilroberta-base-mapa_coarse-ner |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co./distilroberta-base) on the lextreme dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1020 |
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- Precision: 0.7441 |
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- Recall: 0.5805 |
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- F1: 0.6522 |
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- Accuracy: 0.9872 |
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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: 15 |
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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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| 0.0343 | 1.0 | 1739 | 0.0694 | 0.6342 | 0.5205 | 0.5718 | 0.9841 | |
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| 0.0263 | 2.0 | 3478 | 0.0705 | 0.7961 | 0.5235 | 0.6317 | 0.9865 | |
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| 0.0183 | 3.0 | 5217 | 0.0670 | 0.7417 | 0.5313 | 0.6191 | 0.9864 | |
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| 0.015 | 4.0 | 6956 | 0.0632 | 0.7237 | 0.5850 | 0.6470 | 0.9869 | |
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| 0.0137 | 5.0 | 8695 | 0.0663 | 0.7311 | 0.6064 | 0.6629 | 0.9872 | |
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| 0.011 | 6.0 | 10434 | 0.0703 | 0.7163 | 0.5877 | 0.6457 | 0.9868 | |
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| 0.0096 | 7.0 | 12173 | 0.0799 | 0.7511 | 0.5676 | 0.6466 | 0.9871 | |
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| 0.0071 | 8.0 | 13912 | 0.0770 | 0.7386 | 0.5640 | 0.6396 | 0.9868 | |
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| 0.0068 | 9.0 | 15651 | 0.0827 | 0.7285 | 0.5674 | 0.6379 | 0.9868 | |
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| 0.0057 | 10.0 | 17390 | 0.0897 | 0.7611 | 0.5719 | 0.6531 | 0.9872 | |
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| 0.0053 | 11.0 | 19129 | 0.0940 | 0.7614 | 0.5627 | 0.6471 | 0.9871 | |
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| 0.004 | 12.0 | 20868 | 0.0874 | 0.7184 | 0.6084 | 0.6588 | 0.9873 | |
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| 0.0035 | 13.0 | 22607 | 0.0986 | 0.7513 | 0.5766 | 0.6525 | 0.9872 | |
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| 0.003 | 14.0 | 24346 | 0.1012 | 0.7396 | 0.5805 | 0.6505 | 0.9871 | |
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| 0.0026 | 15.0 | 26085 | 0.1020 | 0.7441 | 0.5805 | 0.6522 | 0.9872 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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