distilbert-base-uncased-tokenclassification
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: 0.2495
- Precision: 0.6422
- Recall: 0.4294
- F1: 0.5147
- Accuracy: 0.9472
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 | 0.2653 | 0.6479 | 0.3433 | 0.4488 | 0.9418 |
| No log | 2.0 | 426 | 0.2495 | 0.6422 | 0.4294 | 0.5147 | 0.9472 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for Yeji-Seong/distilbert-base-uncased-tokenclassification
Base model
distilbert/distilbert-base-uncased