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

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: 0.1009
- Precision: 0.9579
- Recall: 0.9512
- F1: 0.9546
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1608        | 1.0   | 798  | 0.2575          | 0.8881    | 0.8916 | 0.8898 | 0.9532   |
| 0.1561        | 2.0   | 1596 | 0.1188          | 0.9459    | 0.9389 | 0.9424 | 0.9806   |
| 0.0979        | 3.0   | 2394 | 0.1060          | 0.9507    | 0.9486 | 0.9497 | 0.9838   |
| 0.0579        | 4.0   | 3192 | 0.0916          | 0.9573    | 0.9475 | 0.9524 | 0.9851   |
| 0.0507        | 5.0   | 3990 | 0.1109          | 0.9527    | 0.9506 | 0.9516 | 0.9839   |
| 0.0344        | 6.0   | 4788 | 0.0987          | 0.9575    | 0.9488 | 0.9531 | 0.9855   |
| 0.0266        | 7.0   | 5586 | 0.1010          | 0.9584    | 0.9501 | 0.9542 | 0.9854   |
| 0.0211        | 8.0   | 6384 | 0.1051          | 0.9575    | 0.9498 | 0.9536 | 0.9855   |
| 0.0168        | 9.0   | 7182 | 0.1009          | 0.9577    | 0.9516 | 0.9546 | 0.9861   |
| 0.016         | 10.0  | 7980 | 0.1009          | 0.9579    | 0.9512 | 0.9546 | 0.9860   |


### Framework versions

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