<!-- 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. -->
roberta-large-finetuned-combined-DS
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.2062
- Accuracy: 0.7001
- Precision: 0.6703
- Recall: 0.6700
- F1: 0.6701
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.8804 | 1.0 | 711 | 0.8517 | 0.6573 | 0.6786 | 0.6253 | 0.6231 |
| 0.6949 | 2.0 | 1422 | 0.7444 | 0.6833 | 0.6609 | 0.6647 | 0.6604 |
| 0.5674 | 3.0 | 2133 | 0.8379 | 0.6798 | 0.6571 | 0.6659 | 0.6575 |
| 0.433 | 3.99 | 2844 | 0.8703 | 0.7079 | 0.6947 | 0.6801 | 0.6809 |
| 0.3314 | 4.99 | 3555 | 1.1792 | 0.6861 | 0.6672 | 0.6558 | 0.6569 |
| 0.2519 | 5.99 | 4266 | 1.5574 | 0.6966 | 0.6761 | 0.6639 | 0.6662 |
| 0.2083 | 6.99 | 4977 | 1.8781 | 0.6952 | 0.6681 | 0.6592 | 0.6619 |
| 0.1773 | 7.99 | 5688 | 1.8687 | 0.6959 | 0.6677 | 0.6748 | 0.6675 |
| 0.1536 | 8.99 | 6399 | 2.2483 | 0.7037 | 0.6788 | 0.6674 | 0.6694 |
| 0.1305 | 9.99 | 7110 | 2.4602 | 0.6875 | 0.6597 | 0.6681 | 0.6612 |
| 0.0982 | 10.98 | 7821 | 2.5573 | 0.6994 | 0.6705 | 0.6728 | 0.6709 |
| 0.0858 | 11.98 | 8532 | 2.8048 | 0.6994 | 0.6765 | 0.6730 | 0.6737 |
| 0.0734 | 12.98 | 9243 | 3.0408 | 0.6945 | 0.6640 | 0.6628 | 0.6626 |
| 0.0625 | 13.98 | 9954 | 3.0047 | 0.7037 | 0.6784 | 0.6757 | 0.6764 |
| 0.0434 | 14.98 | 10665 | 3.0789 | 0.6987 | 0.6737 | 0.6669 | 0.6691 |
| 0.0432 | 15.98 | 11376 | 2.9647 | 0.6945 | 0.6649 | 0.6684 | 0.6663 |
| 0.0326 | 16.98 | 12087 | 3.3076 | 0.6931 | 0.6630 | 0.6563 | 0.6583 |
| 0.032 | 17.97 | 12798 | 3.1890 | 0.7022 | 0.6737 | 0.6702 | 0.6717 |
| 0.0275 | 18.97 | 13509 | 3.1798 | 0.7029 | 0.6738 | 0.6750 | 0.6744 |
| 0.0251 | 19.97 | 14220 | 3.2062 | 0.7001 | 0.6703 | 0.6700 | 0.6701 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
