---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Dung111/distilbert-base-uncased-finetuned-squad
  results: []
---

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# Dung111/distilbert-base-uncased-finetuned-squad

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: 2.8629
- Train End Logits Accuracy: 0.2996
- Train Start Logits Accuracy: 0.2862
- Validation Loss: 2.8396
- Validation End Logits Accuracy: 0.3094
- Validation Start Logits Accuracy: 0.3183
- 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': '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': 2e-05, 'decay_steps': 252, '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 |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 4.3395     | 0.0799                    | 0.0714                      | 3.5811          | 0.1680                         | 0.1473                           | 0     |
| 2.8629     | 0.2996                    | 0.2862                      | 2.8396          | 0.3094                         | 0.3183                           | 1     |


### Framework versions

- Transformers 4.44.0
- TensorFlow 2.17.0
- Datasets 2.21.0
- Tokenizers 0.19.1