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roberta-large-gest-pred-seqeval-partialmatch
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6296
- Precision: 07789
- Recall: 0.7815
- F1: 0.7741
- Accuracy: 0.8331
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.7374 | 1.0 | 147 | 0.9232 | 0.5124 | 0.4475 | 0.4382 | 0.7460 |
| 0.7794 | 2.0 | 294 | 0.6632 | 0.7150 | 0.6640 | 0.6752 | 0.8063 |
| 0.478 | 3.0 | 441 | 0.6320 | 0.7630 | 0.8022 | 0.7693 | 0.8385 |
| 0.305 | 4.0 | 588 | 0.6296 | 0.7789 | 0.7815 | 0.7741 | 0.8331 |
| 0.1967 | 5.0 | 735 | 0.6706 | 0.7531 | 0.7203 | 0.7187 | 0.8445 |
| 0.137 | 6.0 | 882 | 0.7675 | 0.7634 | 0.6827 | 0.6838 | 0.8458 |
| 0.0896 | 7.0 | 1029 | 0.8077 | 0.7995 | 0.7612 | 0.7559 | 0.8499 |
| 0.0583 | 8.0 | 1176 | 0.8361 | 0.7164 | 0.7296 | 0.6980 | 0.8291 |
| 0.0375 | 9.0 | 1323 | 0.8315 | 0.7769 | 0.7601 | 0.7364 | 0.8546 |
| 0.027 | 10.0 | 1470 | 0.8609 | 0.7683 | 0.7579 | 0.7303 | 0.8452 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
LICENSE
Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados. Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigación Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a título enunciativo pero no limitativo, la reproducción, fijación, distribución, comunicación pública, ingeniería inversa y/o transformación sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado también responsable de las consecuencias legales que pudieran derivarse de sus actos.
