binary-stock-classifier
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1577
- Accuracy: 0.95
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 90 | 0.0497 | 1.0 |
| No log | 2.0 | 180 | 0.0138 | 1.0 |
| No log | 3.0 | 270 | 0.4034 | 0.9 |
| No log | 4.0 | 360 | 0.2492 | 0.95 |
| No log | 5.0 | 450 | 0.1985 | 0.95 |
| 0.1536 | 6.0 | 540 | 0.0427 | 0.95 |
| 0.1536 | 7.0 | 630 | 0.2490 | 0.95 |
| 0.1536 | 8.0 | 720 | 0.1568 | 0.95 |
| 0.1536 | 9.0 | 810 | 0.1577 | 0.95 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
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Base model
distilbert/distilbert-base-uncased