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bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0650
- Precision: 0.9847
- Recall: 0.9864
- F1: 0.9856
- Accuracy: 0.9719
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 25 | 0.2530 | 0.9106 | 0.8321 | 0.8696 | 0.7793 |
| No log | 2.0 | 50 | 0.1882 | 0.9855 | 0.6891 | 0.8111 | 0.7116 |
| No log | 3.0 | 75 | 0.1879 | 0.9467 | 0.7173 | 0.8162 | 0.7105 |
| No log | 4.0 | 100 | 0.1987 | 0.9567 | 0.7108 | 0.8156 | 0.7120 |
| No log | 5.0 | 125 | 0.1949 | 0.9511 | 0.7136 | 0.8154 | 0.7105 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
