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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: edyfjm07/distilbert-base-uncased-QA2-finetuned-squad-es
results: []
---
<!-- 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-QA2-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: 0.0399
- Train End Logits Accuracy: 0.9821
- Train Start Logits Accuracy: 0.9884
- Validation Loss: 1.5254
- Validation End Logits Accuracy: 0.7868
- Validation Start Logits Accuracy: 0.7931
- Epoch: 24
## 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': 0.0001, 'decay_steps': 5474, '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 |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 2.3428 | 0.4160 | 0.4317 | 1.3438 | 0.5611 | 0.6458 | 0 |
| 1.1526 | 0.6261 | 0.6397 | 1.0597 | 0.6677 | 0.7429 | 1 |
| 0.7612 | 0.7269 | 0.7647 | 1.0245 | 0.7210 | 0.7806 | 2 |
| 0.5528 | 0.7836 | 0.8319 | 1.2436 | 0.7116 | 0.7712 | 3 |
| 0.4667 | 0.8340 | 0.8435 | 1.0705 | 0.7524 | 0.7555 | 4 |
| 0.3834 | 0.8813 | 0.8687 | 1.1209 | 0.7586 | 0.7712 | 5 |
| 0.3678 | 0.8634 | 0.8876 | 1.2341 | 0.7618 | 0.7649 | 6 |
| 0.2555 | 0.9044 | 0.9181 | 1.1561 | 0.7649 | 0.8056 | 7 |
| 0.2151 | 0.9160 | 0.9328 | 1.0908 | 0.7931 | 0.7994 | 8 |
| 0.1855 | 0.9286 | 0.9475 | 1.2809 | 0.7994 | 0.7774 | 9 |
| 0.1654 | 0.9443 | 0.9454 | 1.3974 | 0.7837 | 0.7806 | 10 |
| 0.1282 | 0.9464 | 0.9517 | 1.4260 | 0.7774 | 0.7837 | 11 |
| 0.1313 | 0.9443 | 0.9601 | 1.4537 | 0.7900 | 0.7962 | 12 |
| 0.1301 | 0.9517 | 0.9590 | 1.1851 | 0.7774 | 0.8150 | 13 |
| 0.1089 | 0.9548 | 0.9590 | 1.2442 | 0.7774 | 0.8088 | 14 |
| 0.1023 | 0.9601 | 0.9622 | 1.4575 | 0.7931 | 0.7931 | 15 |
| 0.0956 | 0.9590 | 0.9685 | 1.5160 | 0.7837 | 0.7900 | 16 |
| 0.0712 | 0.9727 | 0.9737 | 1.5741 | 0.7900 | 0.8088 | 17 |
| 0.0752 | 0.9674 | 0.9790 | 1.4401 | 0.7931 | 0.7994 | 18 |
| 0.0604 | 0.9737 | 0.9779 | 1.6410 | 0.7962 | 0.8088 | 19 |
| 0.0497 | 0.9758 | 0.9821 | 1.5655 | 0.7962 | 0.8119 | 20 |
| 0.0668 | 0.9685 | 0.9811 | 1.3480 | 0.7806 | 0.7962 | 21 |
| 0.0567 | 0.9769 | 0.9800 | 1.3820 | 0.7900 | 0.8088 | 22 |
| 0.0550 | 0.9769 | 0.9832 | 1.3593 | 0.7806 | 0.8056 | 23 |
| 0.0399 | 0.9821 | 0.9884 | 1.5254 | 0.7868 | 0.7931 | 24 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
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