---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mmiteva/qa_model_test
  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_test

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4469
- Train End Logits Accuracy: 0.8470
- Train Start Logits Accuracy: 0.8386
- Validation Loss: 1.0938
- Validation End Logits Accuracy: 0.7318
- Validation Start Logits Accuracy: 0.7255
- 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': 108280, '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 |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.4847     | 0.5934                    | 0.5787                      | 1.1159          | 0.6724                         | 0.6590                           | 0     |
| 0.9507     | 0.7042                    | 0.6909                      | 1.0094          | 0.6973                         | 0.6875                           | 1     |
| 0.7253     | 0.7637                    | 0.7515                      | 0.9841          | 0.7182                         | 0.7124                           | 2     |
| 0.5678     | 0.8090                    | 0.7986                      | 1.0107          | 0.7260                         | 0.7194                           | 3     |
| 0.4469     | 0.8470                    | 0.8386                      | 1.0938          | 0.7318                         | 0.7255                           | 4     |


### Framework versions

- Transformers 4.20.1
- TensorFlow 2.9.2
- Datasets 2.1.0
- Tokenizers 0.12.1