---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base-mar
  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. -->

# xlm-roberta-base-mar

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: 0.1683
- Accuracy: 0.5801
- F1 Binary: 0.2557
- Precision: 0.1758
- Recall: 0.4686

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 36
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:---------:|:------:|
| No log        | 1.0   | 363  | 0.1798          | 0.1539   | 0.2667    | 0.1539    | 1.0    |
| 0.1771        | 2.0   | 726  | 0.1632          | 0.4037   | 0.2860    | 0.1753    | 0.7758 |
| 0.1758        | 3.0   | 1089 | 0.1746          | 0.1560   | 0.2668    | 0.1540    | 0.9978 |
| 0.1758        | 4.0   | 1452 | 0.1683          | 0.5801   | 0.2557    | 0.1758    | 0.4686 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0