---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: LeoZZzzZZ/bert-base-uncased-finetuned-fact
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# LeoZZzzZZ/bert-base-uncased-finetuned-fact

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3218
- Validation Loss: 0.6480
- Train Accuracy: 0.7740
- Train Precision: 0.7726
- Train Recall: 0.7740
- Train F1: 0.7725
- Epoch: 1

## 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': 90930, '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 Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:---------------:|:--------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.4913     | 0.5867          | 0.7729         | 0.7707          | 0.7729       | 0.7706   | 0     |
| 0.3218     | 0.6480          | 0.7740         | 0.7726          | 0.7740       | 0.7725   | 1     |


### Framework versions

- Transformers 4.41.0
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1