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DistilBert-finetuned-Hackaton
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1456
- Accuracy: 0.4283
- F1: 0.4344
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 2.3155 | 1.0 | 338 | 2.6640 | 0.33 | 0.3161 |
| 2.2064 | 2.0 | 676 | 2.5991 | 0.3283 | 0.3094 |
| 2.0703 | 3.0 | 1014 | 2.5172 | 0.3467 | 0.3347 |
| 2.0222 | 4.0 | 1352 | 2.4497 | 0.3567 | 0.3434 |
| 1.9197 | 5.0 | 1690 | 2.3951 | 0.375 | 0.3639 |
| 1.8334 | 6.0 | 2028 | 2.3398 | 0.375 | 0.3646 |
| 1.7327 | 7.0 | 2366 | 2.3231 | 0.3833 | 0.3749 |
| 1.6621 | 8.0 | 2704 | 2.3040 | 0.3867 | 0.3787 |
| 1.5902 | 9.0 | 3042 | 2.2702 | 0.3883 | 0.3809 |
| 1.5554 | 10.0 | 3380 | 2.2230 | 0.4167 | 0.4143 |
| 1.5008 | 11.0 | 3718 | 2.2277 | 0.4067 | 0.3999 |
| 1.4451 | 12.0 | 4056 | 2.2023 | 0.4033 | 0.4025 |
| 1.3788 | 13.0 | 4394 | 2.1953 | 0.41 | 0.4066 |
| 1.3418 | 14.0 | 4732 | 2.1774 | 0.4083 | 0.4036 |
| 1.2689 | 15.0 | 5070 | 2.1798 | 0.41 | 0.4123 |
| 1.2495 | 16.0 | 5408 | 2.1700 | 0.4233 | 0.4228 |
| 1.1946 | 17.0 | 5746 | 2.1653 | 0.42 | 0.4241 |
| 1.1652 | 18.0 | 6084 | 2.1672 | 0.4283 | 0.4279 |
| 1.1428 | 19.0 | 6422 | 2.1631 | 0.4217 | 0.4259 |
| 1.1027 | 20.0 | 6760 | 2.1501 | 0.4133 | 0.4189 |
| 1.063 | 21.0 | 7098 | 2.1522 | 0.4183 | 0.4244 |
| 1.0621 | 22.0 | 7436 | 2.1480 | 0.42 | 0.4258 |
| 1.0412 | 23.0 | 7774 | 2.1491 | 0.4217 | 0.4285 |
| 1.0311 | 24.0 | 8112 | 2.1493 | 0.4267 | 0.4333 |
| 1.0195 | 25.0 | 8450 | 2.1456 | 0.4283 | 0.4344 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
