---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned_roberta-base
  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. -->

# finetuned_roberta-base

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2574
- Accuracy: 0.6033

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.48          | 1.0   | 75   | 1.4001          | 0.41     |
| 1.2847        | 2.0   | 150  | 1.1993          | 0.58     |
| 1.1522        | 3.0   | 225  | 1.0007          | 0.6333   |
| 0.9921        | 4.0   | 300  | 0.9189          | 0.66     |
| 0.9104        | 5.0   | 375  | 0.8855          | 0.69     |
| 0.8371        | 6.0   | 450  | 0.9431          | 0.6767   |
| 0.699         | 7.0   | 525  | 0.9500          | 0.6633   |
| 0.6872        | 8.0   | 600  | 0.9728          | 0.7033   |
| 0.5867        | 9.0   | 675  | 0.9939          | 0.6867   |
| 0.5323        | 10.0  | 750  | 1.1115          | 0.69     |
| 0.4066        | 11.0  | 825  | 1.2031          | 0.6667   |
| 0.3517        | 12.0  | 900  | 1.2193          | 0.65     |
| 0.3114        | 13.0  | 975  | 1.2281          | 0.67     |
| 0.3102        | 14.0  | 1050 | 1.2691          | 0.67     |
| 0.2681        | 15.0  | 1125 | 1.2818          | 0.6633   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1