NLP-HW5-PosTaggerModel
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: 0.1278
- Accuracy: 0.9659
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7026 | 1.0 | 878 | 0.1925 | 0.9493 |
| 0.1976 | 2.0 | 1756 | 0.1446 | 0.9610 |
| 0.157 | 3.0 | 2634 | 0.1278 | 0.9659 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for Pedrampd/NLP-HW5-PosTaggerModel
Base model
google-bert/bert-base-uncased