---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fedcsis-intent_baseline-xlm_r-en
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# fedcsis-intent_baseline-xlm_r-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[leyzer-fedcsis](https://huggingface.co/datasets/cartesinus/leyzer-fedcsis) dataset.
Results on test set:
- Accuracy: **0.904007**

It achieves the following results on the evaluation set:
- Loss: 0.1286
- Accuracy: **0.9772**

## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 3.4583        | 1.0   | 814  | 1.7712          | 0.6193   | 0.6193 |
| 1.3828        | 2.0   | 1628 | 0.9693          | 0.8073   | 0.8073 |
| 0.9585        | 3.0   | 2442 | 0.5830          | 0.8893   | 0.8893 |
| 0.502         | 4.0   | 3256 | 0.3813          | 0.9295   | 0.9295 |
| 0.2907        | 5.0   | 4070 | 0.2699          | 0.9485   | 0.9485 |
| 0.2267        | 6.0   | 4884 | 0.2059          | 0.9615   | 0.9615 |
| 0.1437        | 7.0   | 5698 | 0.1648          | 0.9700   | 0.9700 |
| 0.0998        | 8.0   | 6512 | 0.1422          | 0.9741   | 0.9741 |
| 0.0856        | 9.0   | 7326 | 0.1334          | 0.9758   | 0.9758 |
| 0.0748        | 10.0  | 8140 | 0.1286          | 0.9772   | 0.9772 |


### Framework versions

- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2