---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: tmpr6kbd572
  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. -->

# tmpr6kbd572

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: 0.5683
- Accuracy: 0.8766
- Precision: 0.9064
- Recall: 0.8907
- F1: 0.8985

## 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: 8
- eval_batch_size: 16
- seed: 1234
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3981        | 0.9993 | 737  | 0.2998          | 0.8709   | 0.8879    | 0.9035 | 0.8956 |
| 0.2632        | 2.0    | 1475 | 0.3388          | 0.8734   | 0.8915    | 0.9035 | 0.8975 |
| 0.1902        | 2.9993 | 2212 | 0.4845          | 0.8791   | 0.8917    | 0.9139 | 0.9027 |
| 0.1397        | 3.9973 | 2948 | 0.5548          | 0.8823   | 0.8987    | 0.9108 | 0.9047 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.20.3