---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fedcsis-slot_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-slot_baseline-xlm_r-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1015
- Precision: 0.9723
- Recall: 0.9726
- F1: 0.9725
- Accuracy: 0.9860

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2866        | 1.0   | 814  | 0.3188          | 0.8661    | 0.8672 | 0.8666 | 0.9250   |
| 0.1956        | 2.0   | 1628 | 0.1299          | 0.9409    | 0.9471 | 0.9440 | 0.9736   |
| 0.1063        | 3.0   | 2442 | 0.1196          | 0.9537    | 0.9607 | 0.9572 | 0.9810   |
| 0.0558        | 4.0   | 3256 | 0.0789          | 0.9661    | 0.9697 | 0.9679 | 0.9854   |
| 0.0367        | 5.0   | 4070 | 0.0824          | 0.9685    | 0.9690 | 0.9687 | 0.9848   |
| 0.031         | 6.0   | 4884 | 0.0887          | 0.9712    | 0.9728 | 0.9720 | 0.9859   |
| 0.0233        | 7.0   | 5698 | 0.0829          | 0.9736    | 0.9744 | 0.9740 | 0.9872   |
| 0.0139        | 8.0   | 6512 | 0.0879          | 0.9743    | 0.9747 | 0.9745 | 0.9876   |
| 0.007         | 9.0   | 7326 | 0.0978          | 0.9740    | 0.9734 | 0.9737 | 0.9870   |
| 0.0076        | 10.0  | 8140 | 0.1015          | 0.9723    | 0.9726 | 0.9725 | 0.9860   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2