---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm-roberta-base-rus-finetuned
  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-rus-finetuned

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: 0.1612
- F1: 0.8438
- Roc Auc: 0.8957
- Accuracy: 0.7839

## 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4558        | 1.0   | 131  | 0.4195          | 0.0    | 0.5     | 0.1608   |
| 0.268         | 2.0   | 262  | 0.2245          | 0.6816 | 0.7919  | 0.6734   |
| 0.198         | 3.0   | 393  | 0.2071          | 0.7838 | 0.8577  | 0.6985   |
| 0.141         | 4.0   | 524  | 0.1801          | 0.8030 | 0.8733  | 0.7487   |
| 0.1185        | 5.0   | 655  | 0.1612          | 0.8438 | 0.8957  | 0.7839   |
| 0.1002        | 6.0   | 786  | 0.1520          | 0.8418 | 0.8999  | 0.7990   |
| 0.0967        | 7.0   | 917  | 0.1678          | 0.8278 | 0.8956  | 0.7688   |
| 0.0673        | 8.0   | 1048 | 0.1673          | 0.8402 | 0.9043  | 0.7487   |
| 0.0535        | 9.0   | 1179 | 0.1721          | 0.8339 | 0.9025  | 0.7638   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0