resume-strength-model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2560
- Accuracy: 0.95
- Precision: 0.95
- Recall: 0.95
- F1: 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 40 | 0.2560 | 0.95 | 0.95 | 0.95 | 0.95 |
| No log | 2.0 | 80 | 0.1306 | 0.95 | 0.95 | 0.95 | 0.95 |
| No log | 3.0 | 120 | 0.1226 | 0.95 | 0.95 | 0.95 | 0.95 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.11.0+cpu
- Datasets 4.8.5
- Tokenizers 0.23.1