distilbert-base-uncased_allagree3
This model is a fine-tuned version of distilbert-base-uncased on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.0937
- Accuracy: 0.9779
- F1: 0.9780
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6418 | 1.0 | 57 | 0.3340 | 0.8805 | 0.8768 |
| 0.1821 | 2.0 | 114 | 0.1088 | 0.9690 | 0.9691 |
| 0.0795 | 3.0 | 171 | 0.0822 | 0.9823 | 0.9823 |
| 0.0385 | 4.0 | 228 | 0.0939 | 0.9646 | 0.9646 |
| 0.0218 | 5.0 | 285 | 0.1151 | 0.9735 | 0.9737 |
| 0.0149 | 6.0 | 342 | 0.1126 | 0.9690 | 0.9694 |
| 0.006 | 7.0 | 399 | 0.0989 | 0.9779 | 0.9780 |
| 0.0093 | 8.0 | 456 | 0.1009 | 0.9779 | 0.9780 |
| 0.0063 | 9.0 | 513 | 0.0899 | 0.9779 | 0.9780 |
| 0.0039 | 10.0 | 570 | 0.0937 | 0.9779 | 0.9780 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.3.2
- Tokenizers 0.12.1
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Dataset used to train Farshid/distilbert-base-uncased_allagree3
Evaluation results
- Accuracy on financial_phrasebankself-reported0.978
- F1 on financial_phrasebankself-reported0.978