---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Chessmen/TF_fine_tune_distilbert-base-uncased
  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. -->

# Chessmen/TF_fine_tune_distilbert-base-uncased

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: 2.5021
- Validation Loss: 2.3874
- Epoch: 9

## 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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -233, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.7036     | 2.4365          | 0     |
| 2.5098     | 2.3908          | 1     |
| 2.5046     | 2.4009          | 2     |
| 2.5038     | 2.3998          | 3     |
| 2.5062     | 2.4074          | 4     |
| 2.5054     | 2.4140          | 5     |
| 2.5049     | 2.4012          | 6     |
| 2.5009     | 2.3839          | 7     |
| 2.5036     | 2.4018          | 8     |
| 2.5021     | 2.3874          | 9     |


### Framework versions

- Transformers 4.44.2
- TensorFlow 2.17.0
- Datasets 2.21.0
- Tokenizers 0.19.1