---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: ac-01-bert-finetuned
  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. -->

# ac-01-bert-finetuned

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0038
- Validation Loss: 0.2972
- Train Recall: 0.8810
- Epoch: 6

## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Recall | Epoch |
|:----------:|:---------------:|:------------:|:-----:|
| 0.4802     | 0.2870          | 0.8651       | 0     |
| 0.2311     | 0.2565          | 0.8413       | 1     |
| 0.1166     | 0.2448          | 0.9206       | 2     |
| 0.0348     | 0.2807          | 0.9286       | 3     |
| 0.0123     | 0.3176          | 0.9444       | 4     |
| 0.0210     | 0.2646          | 0.9206       | 5     |
| 0.0038     | 0.2972          | 0.8810       | 6     |


### Framework versions

- Transformers 4.31.0
- TensorFlow 2.13.0
- Datasets 2.14.4
- Tokenizers 0.13.3