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ES-ENG-mDeBERTa-sentiment
This model is a fine-tuned version of microsoft/mdeberta-v3-base on a Custom dataset.
The best model (stopped after 20 epochs) achieves the following results on the evaluation set:
- Loss:0.7549
- Accuracy: 0.6806
- F1: 0.6783
- Precision: 0.6775
- Recall: 0.6806
Intended uses & limitations
Note that commercial use with this model is prohibited.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 1.0833 | 1.0 | 208 | 1.0218 | 0.3835 | 0.2413 | 0.4619 | 0.3835 |
| 0.9826 | 2.0 | 416 | 0.9220 | 0.5673 | 0.5524 | 0.5583 | 0.5673 |
| 0.8951 | 3.0 | 624 | 0.8435 | 0.5916 | 0.5836 | 0.5853 | 0.5916 |
| 0.8377 | 4.0 | 832 | 0.8200 | 0.5994 | 0.5916 | 0.5931 | 0.5994 |
| 0.8083 | 5.0 | 1040 | 0.8110 | 0.5977 | 0.5960 | 0.5957 | 0.5977 |
| 0.7854 | 6.0 | 1248 | 0.8009 | 0.5994 | 0.5943 | 0.5916 | 0.5994 |
| 0.7699 | 7.0 | 1456 | 0.7919 | 0.6084 | 0.6065 | 0.6055 | 0.6084 |
| 0.7489 | 8.0 | 1664 | 0.7827 | 0.6230 | 0.6164 | 0.6192 | 0.6230 |
| 0.7323 | 9.0 | 1872 | 0.7739 | 0.6272 | 0.6251 | 0.6246 | 0.6272 |
| 0.7162 | 10.0 | 2080 | 0.7725 | 0.6408 | 0.6332 | 0.6351 | 0.6408 |
| 0.6958 | 11.0 | 2288 | 0.7636 | 0.6414 | 0.6393 | 0.6383 | 0.6414 |
| 0.6816 | 12.0 | 2496 | 0.7582 | 0.6495 | 0.6491 | 0.6516 | 0.6495 |
| 0.6706 | 13.0 | 2704 | 0.7492 | 0.6628 | 0.6607 | 0.6595 | 0.6628 |
| 0.6569 | 14.0 | 2912 | 0.7554 | 0.6586 | 0.6580 | 0.6579 | 0.6586 |
| 0.6422 | 15.0 | 3120 | 0.7525 | 0.6676 | 0.6661 | 0.6667 | 0.6676 |
| 0.6408 | 16.0 | 3328 | 0.7527 | 0.6660 | 0.6658 | 0.6669 | 0.6660 |
| 0.6273 | 17.0 | 3536 | 0.7483 | 0.6712 | 0.6700 | 0.6710 | 0.6712 |
| 0.6186 | 18.0 | 3744 | 0.7531 | 0.6748 | 0.6731 | 0.6727 | 0.6748 |
| 0.6107 | 19.0 | 3952 | 0.7482 | 0.6799 | 0.6786 | 0.6782 | 0.6799 |
| 0.6055 | 20.0 | 4160 | 0.7549 | 0.6806 | 0.6783 | 0.6775 | 0.6806 |
| 0.6026 | 21.0 | 4368 | 0.7574 | 0.6725 | 0.6719 | 0.6733 | 0.6725 |
| 0.5906 | 22.0 | 4576 | 0.7587 | 0.6728 | 0.6721 | 0.6723 | 0.6728 |
| 0.5888 | 23.0 | 4784 | 0.7621 | 0.6761 | 0.6756 | 0.6758 | 0.6761 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
