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distilbert-base-uncased-TASTESet-ner
This model is a fine-tuned version of distilbert-base-uncased on the TASTESet dataset. It achieves the following results on the evaluation set:
- Loss: 0.3816
- Precision: 0.8929
- Recall: 0.9229
- F1: 0.9076
- Accuracy: 0.9130
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 31 | 1.0797 | 0.6027 | 0.6903 | 0.6435 | 0.7063 |
| No log | 2.0 | 62 | 0.6402 | 0.7681 | 0.8295 | 0.7976 | 0.8304 |
| No log | 3.0 | 93 | 0.4899 | 0.8379 | 0.8789 | 0.8579 | 0.8728 |
| No log | 4.0 | 124 | 0.4232 | 0.8716 | 0.8994 | 0.8853 | 0.8912 |
| No log | 5.0 | 155 | 0.3883 | 0.8798 | 0.9043 | 0.8919 | 0.8992 |
| No log | 6.0 | 186 | 0.3848 | 0.8769 | 0.9103 | 0.8933 | 0.9004 |
| No log | 7.0 | 217 | 0.3684 | 0.8864 | 0.9123 | 0.8991 | 0.9046 |
| No log | 8.0 | 248 | 0.3650 | 0.8930 | 0.9182 | 0.9054 | 0.9087 |
| No log | 9.0 | 279 | 0.3628 | 0.8908 | 0.9197 | 0.9050 | 0.9096 |
| No log | 10.0 | 310 | 0.3674 | 0.8933 | 0.9165 | 0.9047 | 0.9093 |
| No log | 11.0 | 341 | 0.3668 | 0.8958 | 0.9177 | 0.9066 | 0.9120 |
| No log | 12.0 | 372 | 0.3717 | 0.8904 | 0.9234 | 0.9066 | 0.9120 |
| No log | 13.0 | 403 | 0.3693 | 0.8940 | 0.9197 | 0.9067 | 0.9126 |
| No log | 14.0 | 434 | 0.3805 | 0.8913 | 0.9239 | 0.9073 | 0.9135 |
| No log | 15.0 | 465 | 0.3788 | 0.8954 | 0.9202 | 0.9076 | 0.9123 |
| No log | 16.0 | 496 | 0.3803 | 0.8935 | 0.9231 | 0.9081 | 0.9122 |
| 0.3275 | 17.0 | 527 | 0.3814 | 0.8918 | 0.9229 | 0.9071 | 0.9126 |
| 0.3275 | 18.0 | 558 | 0.3823 | 0.8921 | 0.9241 | 0.9079 | 0.9123 |
| 0.3275 | 19.0 | 589 | 0.3827 | 0.8928 | 0.9224 | 0.9074 | 0.9124 |
| 0.3275 | 20.0 | 620 | 0.3816 | 0.8929 | 0.9229 | 0.9076 | 0.9130 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
