bert-base-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25
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: 0.2698
- Precision: 0.3321
- Recall: 0.5265
- F1: 0.4073
- Accuracy: 0.8942
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 | 30 | 0.3314 | 0.1627 | 0.3746 | 0.2269 | 0.8419 |
| No log | 2.0 | 60 | 0.2957 | 0.2887 | 0.4841 | 0.3617 | 0.8592 |
| No log | 3.0 | 90 | 0.2905 | 0.2429 | 0.5141 | 0.3299 | 0.8651 |
| No log | 4.0 | 120 | 0.2759 | 0.3137 | 0.5565 | 0.4013 | 0.8787 |
| No log | 5.0 | 150 | 0.2977 | 0.3116 | 0.5565 | 0.3995 | 0.8796 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3