---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: ayshi/basic_roberta
  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. -->

# ayshi/basic_roberta

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5088
- Validation Loss: 0.8055
- Train Accuracy: 0.7733
- Epoch: 8

## 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': 320, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.3394     | 1.1667          | 0.6667         | 0     |
| 1.1446     | 1.1147          | 0.6667         | 1     |
| 1.0726     | 1.0547          | 0.6667         | 2     |
| 0.9986     | 1.0134          | 0.6844         | 3     |
| 0.8905     | 0.9697          | 0.7378         | 4     |
| 0.7645     | 0.9131          | 0.7556         | 5     |
| 0.6761     | 0.8459          | 0.76           | 6     |
| 0.5992     | 0.7954          | 0.7778         | 7     |
| 0.5088     | 0.8055          | 0.7733         | 8     |


### Framework versions

- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1