check-in-quality-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: 0.8464
- Accuracy: 0.7143
- F1: 0.6857
- Precision: 0.6746
- Recall: 0.7143
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: 4
- 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 | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 21 | 0.9602 | 0.2857 | 0.1270 | 0.0816 | 0.2857 |
| No log | 2.0 | 42 | 0.8913 | 0.5238 | 0.4891 | 0.6302 | 0.5238 |
| No log | 3.0 | 63 | 0.8464 | 0.7143 | 0.6857 | 0.6746 | 0.7143 |
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0
- Datasets 4.0.0
- Tokenizers 0.22.1