---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm-roberta-base-hin-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-hin-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: 0.1860
- F1: 0.7535
- Roc Auc: 0.8671
- Accuracy: 0.74

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4196        | 1.0   | 109  | 0.3772          | 0.0    | 0.5     | 0.31     |
| 0.3586        | 2.0   | 218  | 0.2947          | 0.1149 | 0.5683  | 0.39     |
| 0.2381        | 3.0   | 327  | 0.2153          | 0.5976 | 0.7550  | 0.65     |
| 0.1881        | 4.0   | 436  | 0.1626          | 0.7613 | 0.8478  | 0.76     |
| 0.1228        | 5.0   | 545  | 0.1616          | 0.7681 | 0.8682  | 0.75     |
| 0.1085        | 6.0   | 654  | 0.1794          | 0.7227 | 0.8415  | 0.71     |
| 0.0865        | 7.0   | 763  | 0.1545          | 0.7622 | 0.8640  | 0.77     |
| 0.0747        | 8.0   | 872  | 0.1624          | 0.7701 | 0.8739  | 0.74     |
| 0.0561        | 9.0   | 981  | 0.1982          | 0.7479 | 0.8578  | 0.73     |
| 0.0432        | 10.0  | 1090 | 0.1725          | 0.7579 | 0.8643  | 0.74     |
| 0.0418        | 11.0  | 1199 | 0.1910          | 0.7533 | 0.8689  | 0.72     |
| 0.0327        | 12.0  | 1308 | 0.1860          | 0.7535 | 0.8671  | 0.74     |


### Framework versions

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