---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: XLM_CITA
  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_CITA

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5616
- Accuracy: 0.7705
- F1: 0.7698

## 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: 32
- eval_batch_size: 32
- 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: cosine_with_restarts
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6485        | 1.0   | 250  | 0.6020          | 0.6645   | 0.6490 |
| 0.5652        | 2.0   | 500  | 0.5210          | 0.7395   | 0.7397 |
| 0.5122        | 3.0   | 750  | 0.5111          | 0.7495   | 0.7496 |
| 0.4661        | 4.0   | 1000 | 0.5370          | 0.7685   | 0.7684 |
| 0.4244        | 5.0   | 1250 | 0.5206          | 0.7635   | 0.7636 |
| 0.3942        | 6.0   | 1500 | 0.5299          | 0.762    | 0.7621 |
| 0.3611        | 7.0   | 1750 | 0.5380          | 0.7695   | 0.7686 |
| 0.3421        | 8.0   | 2000 | 0.5595          | 0.7745   | 0.7736 |
| 0.3362        | 9.0   | 2250 | 0.5596          | 0.7715   | 0.7708 |
| 0.3274        | 10.0  | 2500 | 0.5616          | 0.7705   | 0.7698 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.21.0