|
--- |
|
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: [] |
|
language: |
|
- es |
|
metrics: |
|
- rouge |
|
- f1 |
|
datasets: |
|
- edyfjm07/squad_indicaciones_es |
|
pipeline_tag: question-answering |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should |
|
probably proofread and complete it, then remove this comment. --> |
|
|
|
# edyfjm07/distilbert-base-uncased-QA1-finetuned-squad-es |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Train Loss: 0.2131 |
|
- Train End Logits Accuracy: 0.9224 |
|
- Train Start Logits Accuracy: 0.9310 |
|
- Validation Loss: 1.0588 |
|
- Validation End Logits Accuracy: 0.8088 |
|
- Validation Start Logits Accuracy: 0.8150 |
|
- Epoch: 50 |
|
|
|
## 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 | |
|
| 0.3409 | 0.8675 | 0.8825 | 1.0242 | 0.7900 | 0.8088 | 24 | |
|
| 0.3564 | 0.8610 | 0.8869 | 1.0257 | 0.7900 | 0.7900 | 25 | |
|
| 0.3324 | 0.8578 | 0.9041 | 1.0227 | 0.7837 | 0.8088 | 26 | |
|
| 0.3066 | 0.8858 | 0.9159 | 1.0243 | 0.7900 | 0.8025 | 27 | |
|
| 0.3026 | 0.8804 | 0.9084 | 1.0224 | 0.7774 | 0.8088 | 28 | |
|
| 0.2896 | 0.8879 | 0.9009 | 1.0324 | 0.7649 | 0.8182 | 29 | |
|
| 0.2710 | 0.8998 | 0.9106 | 1.0458 | 0.7868 | 0.8088 | 30 | |
|
| 0.2727 | 0.8933 | 0.9213 | 1.0483 | 0.7806 | 0.7931 | 31 | |
|
| 0.2728 | 0.8976 | 0.9062 | 1.0459 | 0.7868 | 0.8088 | 32 | |
|
| 0.2780 | 0.8847 | 0.9073 | 1.0595 | 0.7962 | 0.8056 | 33 | |
|
| 0.2641 | 0.8955 | 0.9138 | 1.0503 | 0.7868 | 0.8025 | 34 | |
|
| 0.2611 | 0.9009 | 0.9203 | 1.0458 | 0.8025 | 0.7962 | 35 | |
|
| 0.2502 | 0.9030 | 0.9203 | 1.0621 | 0.8025 | 0.8025 | 36 | |
|
| 0.2655 | 0.8804 | 0.9213 | 1.0478 | 0.7994 | 0.7994 | 37 | |
|
| 0.2434 | 0.9084 | 0.9181 | 1.0491 | 0.8025 | 0.7994 | 38 | |
|
| 0.2409 | 0.9149 | 0.9224 | 1.0452 | 0.8025 | 0.8088 | 39 | |
|
| 0.2271 | 0.9181 | 0.9246 | 1.0487 | 0.7962 | 0.8119 | 40 | |
|
| 0.2288 | 0.9332 | 0.9149 | 1.0579 | 0.8056 | 0.8056 | 41 | |
|
| 0.2444 | 0.9127 | 0.9127 | 1.0522 | 0.8056 | 0.8119 | 42 | |
|
| 0.2145 | 0.9235 | 0.9300 | 1.0584 | 0.8025 | 0.8088 | 43 | |
|
| 0.2264 | 0.9073 | 0.9289 | 1.0520 | 0.8025 | 0.8119 | 44 | |
|
| 0.2120 | 0.9213 | 0.9429 | 1.0591 | 0.8119 | 0.8088 | 45 | |
|
| 0.2280 | 0.9127 | 0.9235 | 1.0538 | 0.8088 | 0.8056 | 46 | |
|
| 0.2166 | 0.9116 | 0.9203 | 1.0554 | 0.8088 | 0.8088 | 47 | |
|
| 0.2184 | 0.9138 | 0.9397 | 1.0568 | 0.8119 | 0.8088 | 48 | |
|
| 0.2087 | 0.9106 | 0.9375 | 1.0588 | 0.8088 | 0.8150 | 49 | |
|
| 0.2131 | 0.9224 | 0.9310 | 1.0588 | 0.8088 | 0.8150 | 50 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- TensorFlow 2.15.0 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
