metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: edyfjm07/distilbert-base-uncased-QA1-finetuned-squad-es
results: []
edyfjm07/distilbert-base-uncased-QA1-finetuned-squad-es
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: 0.3672
- Train End Logits Accuracy: 0.8524
- Train Start Logits Accuracy: 0.8836
- Validation Loss: 1.0040
- Validation End Logits Accuracy: 0.7837
- Validation Start Logits Accuracy: 0.7994
- Epoch: 23
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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 1479, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
---|---|---|---|---|---|---|
5.1787 | 0.0571 | 0.0496 | 4.3181 | 0.1724 | 0.1818 | 0 |
3.6307 | 0.25 | 0.1810 | 2.8944 | 0.3793 | 0.2476 | 1 |
2.5094 | 0.3998 | 0.3147 | 2.1436 | 0.4514 | 0.3793 | 2 |
1.9078 | 0.4871 | 0.4397 | 1.7322 | 0.5204 | 0.5705 | 3 |
1.5135 | 0.5593 | 0.5700 | 1.4332 | 0.6050 | 0.6238 | 4 |
1.2802 | 0.5927 | 0.6013 | 1.3274 | 0.6270 | 0.6364 | 5 |
1.1079 | 0.6595 | 0.6455 | 1.2126 | 0.6520 | 0.6865 | 6 |
0.9827 | 0.6843 | 0.7069 | 1.1469 | 0.7116 | 0.7116 | 7 |
0.8810 | 0.7306 | 0.7371 | 1.0859 | 0.7116 | 0.7053 | 8 |
0.8194 | 0.7349 | 0.7446 | 1.0339 | 0.7429 | 0.7492 | 9 |
0.7245 | 0.7403 | 0.7877 | 1.0371 | 0.7304 | 0.7398 | 10 |
0.6827 | 0.7683 | 0.7856 | 1.0185 | 0.7492 | 0.7461 | 11 |
0.6421 | 0.7866 | 0.8071 | 1.0298 | 0.7492 | 0.7555 | 12 |
0.5949 | 0.8006 | 0.8050 | 0.9877 | 0.7586 | 0.7774 | 13 |
0.5471 | 0.8125 | 0.8244 | 0.9933 | 0.7398 | 0.7774 | 14 |
0.5119 | 0.8233 | 0.8362 | 0.9956 | 0.7524 | 0.7837 | 15 |
0.4916 | 0.8330 | 0.8599 | 0.9917 | 0.7398 | 0.8025 | 16 |
0.4521 | 0.8373 | 0.8836 | 0.9698 | 0.7680 | 0.7868 | 17 |
0.4424 | 0.8459 | 0.8696 | 0.9951 | 0.7712 | 0.8025 | 18 |
0.3928 | 0.8599 | 0.8966 | 1.0173 | 0.7618 | 0.7931 | 19 |
0.3874 | 0.8578 | 0.8922 | 1.0307 | 0.7649 | 0.7931 | 20 |
0.3822 | 0.8588 | 0.8901 | 1.0272 | 0.7680 | 0.7900 | 21 |
0.3859 | 0.8524 | 0.8879 | 1.0180 | 0.7555 | 0.7962 | 22 |
0.3672 | 0.8524 | 0.8836 | 1.0040 | 0.7837 | 0.7994 | 23 |
Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1