autoevaluator
HF staff
Add evaluation results on the lener_br config and train split of lener_br
cb24cd9
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - lener_br | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: xlm-roberta-large-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: 0.8545767716535433 | |
| - name: Recall | |
| type: recall | |
| value: 0.8976479710519514 | |
| - name: F1 | |
| type: f1 | |
| value: 0.8755830076893987 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.979126510974644 | |
| - task: | |
| type: token-classification | |
| name: Token Classification | |
| dataset: | |
| name: lener_br | |
| type: lener_br | |
| config: lener_br | |
| split: test | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9842606502473917 | |
| verified: true | |
| - name: Precision | |
| type: precision | |
| value: 0.9880888491353608 | |
| verified: true | |
| - name: Recall | |
| type: recall | |
| value: 0.9863977974551678 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 0.9872425991435487 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 0.12697908282279968 | |
| verified: true | |
| - 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: 0.979126510974644 | |
| verified: true | |
| - name: Precision | |
| type: precision | |
| value: 0.9846948786709399 | |
| verified: true | |
| - name: Recall | |
| type: recall | |
| value: 0.9839386958155646 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 0.9843166420124387 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 0.17586557567119598 | |
| verified: true | |
| - task: | |
| type: token-classification | |
| name: Token Classification | |
| dataset: | |
| name: lener_br | |
| type: lener_br | |
| config: lener_br | |
| split: train | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9986508230532317 | |
| verified: true | |
| - name: Precision | |
| type: precision | |
| value: 0.9980332928982356 | |
| verified: true | |
| - name: Recall | |
| type: recall | |
| value: 0.998726011303645 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 0.998379531941543 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 0.002737082075327635 | |
| 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 | |
| This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lener_br dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| - Precision: 0.8546 | |
| - Recall: 0.8976 | |
| - F1: 0.8756 | |
| - Accuracy: 0.9791 | |
| ## 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 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.0836 | 1.0 | 3914 | nan | 0.5735 | 0.8348 | 0.6799 | 0.9526 | | |
| | 0.0664 | 2.0 | 7828 | nan | 0.8153 | 0.8315 | 0.8233 | 0.9658 | | |
| | 0.0505 | 3.0 | 11742 | nan | 0.6885 | 0.9147 | 0.7857 | 0.9644 | | |
| | 0.1165 | 4.0 | 15656 | nan | 0.7572 | 0.8067 | 0.7811 | 0.9641 | | |
| | 0.0206 | 5.0 | 19570 | nan | 0.8678 | 0.8770 | 0.8723 | 0.9774 | | |
| | 0.02 | 6.0 | 23484 | nan | 0.7285 | 0.8907 | 0.8015 | 0.9669 | | |
| | 0.0248 | 7.0 | 27398 | nan | 0.8717 | 0.9095 | 0.8902 | 0.9793 | | |
| | 0.0223 | 8.0 | 31312 | nan | 0.8407 | 0.8801 | 0.8600 | 0.9766 | | |
| | 0.0084 | 9.0 | 35226 | nan | 0.8354 | 0.8684 | 0.8516 | 0.9705 | | |
| | 0.0067 | 10.0 | 39140 | nan | 0.8312 | 0.9062 | 0.8671 | 0.9753 | | |
| | 0.006 | 11.0 | 43054 | nan | 0.8866 | 0.8953 | 0.8909 | 0.9784 | | |
| | 0.0058 | 12.0 | 46968 | nan | 0.8961 | 0.8987 | 0.8974 | 0.9807 | | |
| | 0.0062 | 13.0 | 50882 | nan | 0.8360 | 0.8785 | 0.8567 | 0.9783 | | |
| | 0.0053 | 14.0 | 54796 | nan | 0.8327 | 0.8749 | 0.8533 | 0.9782 | | |
| | 0.003 | 15.0 | 58710 | nan | 0.8546 | 0.8976 | 0.8756 | 0.9791 | | |
| ### Framework versions | |
| - Transformers 4.23.1 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.6.1 | |
| - Tokenizers 0.13.1 | |