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

# rubakha/xlm-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: 0.1468
- Train Accuracy: 0.944
- Validation Loss: 0.1545
- Validation Accuracy: 0.9440
- Train Precision: 0.9458
- Train Recall: 0.944
- Train F1: 0.9436
- Epoch: 2

## 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': 5000, '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 | Train Accuracy | Validation Loss | Validation Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.8384     | 0.923          | 0.2463          | 0.9230              | 0.9256          | 0.923        | 0.9221   | 0     |
| 0.2072     | 0.936          | 0.1811          | 0.9360              | 0.9379          | 0.936        | 0.9356   | 1     |
| 0.1468     | 0.944          | 0.1545          | 0.9440              | 0.9458          | 0.944        | 0.9436   | 2     |


### Framework versions

- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2