bert-large-uncased-finetuned-filtered-0602
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8409
- Accuracy: 0.1667
- F1: 0.0476
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: 5e-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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.8331 | 1.0 | 3180 | 1.8054 | 0.1667 | 0.0476 |
| 1.8158 | 2.0 | 6360 | 1.8196 | 0.1667 | 0.0476 |
| 1.8088 | 3.0 | 9540 | 1.8059 | 0.1667 | 0.0476 |
| 1.8072 | 4.0 | 12720 | 1.7996 | 0.1667 | 0.0476 |
| 1.8182 | 5.0 | 15900 | 1.7962 | 0.1667 | 0.0476 |
| 1.7993 | 6.0 | 19080 | 1.8622 | 0.1667 | 0.0476 |
| 1.7963 | 7.0 | 22260 | 1.8378 | 0.1667 | 0.0476 |
| 1.7956 | 8.0 | 25440 | 1.8419 | 0.1667 | 0.0476 |
| 1.7913 | 9.0 | 28620 | 1.8406 | 0.1667 | 0.0476 |
| 1.7948 | 10.0 | 31800 | 1.8409 | 0.1667 | 0.0476 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.12.1