---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mmiteva/qa_model-customs
  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

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: 0.3517
- Train End Logits Accuracy: 0.8772
- Train Start Logits Accuracy: 0.8735
- Validation Loss: 0.8793
- Validation End Logits Accuracy: 0.7642
- Validation Start Logits Accuracy: 0.7586
- Epoch: 4

## 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': 32050, '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 |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.3795     | 0.6168                    | 0.6015                      | 0.9590          | 0.7074                         | 0.6950                           | 0     |
| 0.8193     | 0.7377                    | 0.7260                      | 0.8504          | 0.7313                         | 0.7260                           | 1     |
| 0.5982     | 0.8004                    | 0.7932                      | 0.8225          | 0.7505                         | 0.7440                           | 2     |
| 0.4467     | 0.8462                    | 0.8405                      | 0.8469          | 0.7633                         | 0.7584                           | 3     |
| 0.3517     | 0.8772                    | 0.8735                      | 0.8793          | 0.7642                         | 0.7586                           | 4     |


### Framework versions

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