autoevaluator
HF staff
Add evaluation results on the lener_br config and validation split of lener_br
787c714
| language: | |
| - pt | |
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - lener_br | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: xlm-roberta-base-finetuned-lener_br-finetuned-lener-br | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| dataset: | |
| name: lener_br | |
| type: lener_br | |
| config: lener_br | |
| split: train | |
| args: lener_br | |
| metrics: | |
| - name: Precision | |
| type: precision | |
| value: 0.9206349206349206 | |
| - name: Recall | |
| type: recall | |
| value: 0.9294391315585423 | |
| - name: F1 | |
| type: f1 | |
| value: 0.925016077170418 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9832504071600401 | |
| - task: | |
| type: token-classification | |
| name: Token Classification | |
| dataset: | |
| name: lener_br | |
| type: lener_br | |
| config: lener_br | |
| split: validation | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9832802904657313 | |
| verified: true | |
| - name: Precision | |
| type: precision | |
| value: 0.986258771429967 | |
| verified: true | |
| - name: Recall | |
| type: recall | |
| value: 0.9897717432152019 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 0.9880121346555324 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 0.1050868034362793 | |
| verified: true | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # xlm-roberta-base-finetuned-lener_br-finetuned-lener-br | |
| This model is a fine-tuned version of [Luciano/xlm-roberta-base-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-base-finetuned-lener_br) on the lener_br dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| - Precision: 0.9206 | |
| - Recall: 0.9294 | |
| - F1: 0.9250 | |
| - Accuracy: 0.9833 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 15 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.0657 | 1.0 | 1957 | nan | 0.7780 | 0.8687 | 0.8209 | 0.9718 | | |
| | 0.0321 | 2.0 | 3914 | nan | 0.8755 | 0.8708 | 0.8731 | 0.9793 | | |
| | 0.0274 | 3.0 | 5871 | nan | 0.8096 | 0.9124 | 0.8579 | 0.9735 | | |
| | 0.0216 | 4.0 | 7828 | nan | 0.7913 | 0.8842 | 0.8352 | 0.9718 | | |
| | 0.0175 | 5.0 | 9785 | nan | 0.7735 | 0.9248 | 0.8424 | 0.9721 | | |
| | 0.0117 | 6.0 | 11742 | nan | 0.9206 | 0.9294 | 0.9250 | 0.9833 | | |
| | 0.0121 | 7.0 | 13699 | nan | 0.8988 | 0.9318 | 0.9150 | 0.9819 | | |
| | 0.0086 | 8.0 | 15656 | nan | 0.8922 | 0.9175 | 0.9047 | 0.9801 | | |
| | 0.007 | 9.0 | 17613 | nan | 0.8482 | 0.8997 | 0.8732 | 0.9769 | | |
| | 0.0051 | 10.0 | 19570 | nan | 0.8730 | 0.9274 | 0.8994 | 0.9798 | | |
| | 0.0045 | 11.0 | 21527 | nan | 0.9172 | 0.9051 | 0.9111 | 0.9819 | | |
| | 0.0014 | 12.0 | 23484 | nan | 0.9138 | 0.9155 | 0.9147 | 0.9823 | | |
| | 0.0029 | 13.0 | 25441 | nan | 0.9099 | 0.9287 | 0.9192 | 0.9834 | | |
| | 0.0035 | 14.0 | 27398 | nan | 0.9019 | 0.9294 | 0.9155 | 0.9831 | | |
| | 0.0005 | 15.0 | 29355 | nan | 0.8886 | 0.9343 | 0.9109 | 0.9825 | | |
| ### Framework versions | |
| - Transformers 4.23.1 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.6.1 | |
| - Tokenizers 0.13.1 | |