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assignment2_meher_test2
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5438
- Precision: 0.2453
- Recall: 0.1102
- F1: 0.1520
- Accuracy: 0.9379
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 128 | 0.2982 | 0.4615 | 0.0508 | 0.0916 | 0.9405 |
| No log | 2.0 | 256 | 0.3126 | 0.2727 | 0.0763 | 0.1192 | 0.9401 |
| No log | 3.0 | 384 | 0.3359 | 0.1837 | 0.0763 | 0.1078 | 0.9371 |
| 0.1539 | 4.0 | 512 | 0.4334 | 0.2927 | 0.1017 | 0.1509 | 0.9393 |
| 0.1539 | 5.0 | 640 | 0.5133 | 0.2778 | 0.0847 | 0.1299 | 0.9404 |
| 0.1539 | 6.0 | 768 | 0.5375 | 0.2553 | 0.1017 | 0.1455 | 0.9384 |
| 0.1539 | 7.0 | 896 | 0.5017 | 0.2321 | 0.1102 | 0.1494 | 0.9363 |
| 0.0241 | 8.0 | 1024 | 0.5425 | 0.2889 | 0.1102 | 0.1595 | 0.9395 |
| 0.0241 | 9.0 | 1152 | 0.5578 | 0.3333 | 0.1102 | 0.1656 | 0.9402 |
| 0.0241 | 10.0 | 1280 | 0.5438 | 0.2453 | 0.1102 | 0.1520 | 0.9379 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
