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---
license: apache-2.0
base_model: distilbert-base-cased-distilled-squad
tags:
- generated_from_keras_callback
model-index:
- name: Ahmed-Zakaria/distilbert-base-cased-finetuned-squad
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. -->
# Ahmed-Zakaria/distilbert-base-cased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4553
- Train End Logits Accuracy: 0.8655
- Train Start Logits Accuracy: 0.8283
- Validation Loss: 1.2318
- Validation End Logits Accuracy: 0.7094
- Validation Start Logits Accuracy: 0.6784
- Epoch: 1
## 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': 16635, '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 | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.6238 | 0.8207 | 0.7789 | 1.1257 | 0.7123 | 0.6807 | 0 |
| 0.4553 | 0.8655 | 0.8283 | 1.2318 | 0.7094 | 0.6784 | 1 |
### Framework versions
- Transformers 4.36.0
- TensorFlow 2.13.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|