autoevaluator
HF staff
Add evaluation results on the lener_br config and validation split of lener_br
cc6e46c
| language: | |
| - pt | |
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - lener_br | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: xlm-roberta-large-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: 0.9122490993309316 | |
| - name: Recall | |
| type: recall | |
| value: 0.9162574308606876 | |
| - name: F1 | |
| type: f1 | |
| value: 0.9142488716956804 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.982592974434832 | |
| - 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: 0.982592974434832 | |
| verified: true | |
| - name: Precision | |
| type: precision | |
| value: 0.9882345251323615 | |
| verified: true | |
| - name: Recall | |
| type: recall | |
| value: 0.9881214973122232 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 0.9881780079902612 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 0.16145998239517212 | |
| 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-large-finetuned-lener_br-finetuned-lener-br | |
| This model is a fine-tuned version of [Luciano/xlm-roberta-large-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-large-finetuned-lener_br) on the lener_br dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| - Precision: 0.9122 | |
| - Recall: 0.9163 | |
| - F1: 0.9142 | |
| - Accuracy: 0.9826 | |
| ## 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: 2 | |
| - eval_batch_size: 2 | |
| - 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 | | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.068 | 1.0 | 3914 | nan | 0.6196 | 0.8604 | 0.7204 | 0.9568 | | |
| | 0.0767 | 2.0 | 7828 | nan | 0.8270 | 0.8710 | 0.8484 | 0.9693 | | |
| | 0.0257 | 3.0 | 11742 | nan | 0.7243 | 0.9005 | 0.8029 | 0.9639 | | |
| | 0.0193 | 4.0 | 15656 | nan | 0.9010 | 0.8984 | 0.8997 | 0.9821 | | |
| | 0.0156 | 5.0 | 19570 | nan | 0.7150 | 0.9121 | 0.8016 | 0.9641 | | |
| | 0.0165 | 6.0 | 23484 | nan | 0.7640 | 0.8796 | 0.8177 | 0.9691 | | |
| | 0.0225 | 7.0 | 27398 | nan | 0.8851 | 0.9098 | 0.8973 | 0.9803 | | |
| | 0.016 | 8.0 | 31312 | nan | 0.9081 | 0.9015 | 0.9048 | 0.9792 | | |
| | 0.0078 | 9.0 | 35226 | nan | 0.8941 | 0.8863 | 0.8902 | 0.9788 | | |
| | 0.0061 | 10.0 | 39140 | nan | 0.9026 | 0.9002 | 0.9014 | 0.9804 | | |
| | 0.0057 | 11.0 | 43054 | nan | 0.8793 | 0.9018 | 0.8904 | 0.9769 | | |
| | 0.0044 | 12.0 | 46968 | nan | 0.8790 | 0.9033 | 0.8910 | 0.9785 | | |
| | 0.0043 | 13.0 | 50882 | nan | 0.9122 | 0.9163 | 0.9142 | 0.9826 | | |
| | 0.0003 | 14.0 | 54796 | nan | 0.9032 | 0.9070 | 0.9051 | 0.9807 | | |
| | 0.0025 | 15.0 | 58710 | nan | 0.8903 | 0.9085 | 0.8993 | 0.9798 | | |
| ### Framework versions | |
| - Transformers 4.23.1 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.6.1 | |
| - Tokenizers 0.13.1 | |