---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mmiteva/qa_model-customs_optimized
  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. -->

# mmiteva/qa_model-customs_optimized

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: 0.2417
- Train End Logits Accuracy: 0.9192
- Train Start Logits Accuracy: 0.9158
- Validation Loss: 0.6317
- Validation End Logits Accuracy: 0.8375
- Validation Start Logits Accuracy: 0.8303
- Epoch: 3

## 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 50860, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, '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 |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.1671     | 0.6791                    | 0.6668                      | 0.6925          | 0.7903                         | 0.7716                           | 0     |
| 0.5541     | 0.8257                    | 0.8179                      | 0.5747          | 0.8230                         | 0.8090                           | 1     |
| 0.3527     | 0.8856                    | 0.8786                      | 0.5971          | 0.8352                         | 0.8203                           | 2     |
| 0.2417     | 0.9192                    | 0.9158                      | 0.6317          | 0.8375                         | 0.8303                           | 3     |


### Framework versions

- Transformers 4.25.1
- TensorFlow 2.10.1
- Datasets 2.7.1
- Tokenizers 0.12.1