Pavan-124/lwin_winery
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0354
- Validation Loss: 0.0980
- Train Precision: 0.8918
- Train Recall: 0.8986
- Train F1: 0.8952
- Train Accuracy: 0.9696
- Epoch: 2
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5724, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
---|---|---|---|---|---|---|
0.1279 | 0.0885 | 0.8696 | 0.8806 | 0.8751 | 0.9650 | 0 |
0.0613 | 0.0873 | 0.8828 | 0.8924 | 0.8876 | 0.9681 | 1 |
0.0354 | 0.0980 | 0.8918 | 0.8986 | 0.8952 | 0.9696 | 2 |
Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
- 1
Inference API (serverless) is not available, repository is disabled.
Model tree for Pavan-124/lwin_winery
Base model
distilbert/distilbert-base-uncased
Finetuned
this model