bert-wnut-token-classifier
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2771
- Precision: 0.5642
- Recall: 0.3012
- F1: 0.3927
- Accuracy: 0.9407
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2813 | 0.6274 | 0.2762 | 0.3835 | 0.9398 |
| No log | 2.0 | 426 | 0.2771 | 0.5642 | 0.3012 | 0.3927 | 0.9407 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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Model tree for ZappY-AI/bert-wnut-token-classifier
Base model
distilbert/distilbert-base-uncased