---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: dosai/distilbert-sud
  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. -->

# dosai/distilbert-sud

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: 0.0026
- Epoch: 29

## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 40440, '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, 'weight_decay_rate': 0.001}
- training_precision: mixed_float16

### Training results

| Train Loss | Epoch |
|:----------:|:-----:|
| 0.5392     | 0     |
| 0.2639     | 1     |
| 0.1945     | 2     |
| 0.1562     | 3     |
| 0.1274     | 4     |
| 0.1065     | 5     |
| 0.0871     | 6     |
| 0.0714     | 7     |
| 0.0598     | 8     |
| 0.0499     | 9     |
| 0.0436     | 10    |
| 0.0376     | 11    |
| 0.0305     | 12    |
| 0.0286     | 13    |
| 0.0223     | 14    |
| 0.0215     | 15    |
| 0.0210     | 16    |
| 0.0139     | 17    |
| 0.0147     | 18    |
| 0.0119     | 19    |
| 0.0091     | 20    |
| 0.0086     | 21    |
| 0.0077     | 22    |
| 0.0055     | 23    |
| 0.0060     | 24    |
| 0.0047     | 25    |
| 0.0039     | 26    |
| 0.0037     | 27    |
| 0.0028     | 28    |
| 0.0026     | 29    |


### Framework versions

- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3