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

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: 0.5261
- F1: 0.4912
- Roc Auc: 0.6688
- Accuracy: 0.3065

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4984        | 1.0   | 117  | 0.4862          | 0.1155 | 0.5131  | 0.2081   |
| 0.456         | 2.0   | 234  | 0.4323          | 0.2110 | 0.5565  | 0.2145   |
| 0.4207        | 3.0   | 351  | 0.4142          | 0.2856 | 0.5909  | 0.2903   |
| 0.3932        | 4.0   | 468  | 0.4189          | 0.3215 | 0.6145  | 0.2984   |
| 0.3576        | 5.0   | 585  | 0.4417          | 0.3400 | 0.6254  | 0.3048   |
| 0.324         | 6.0   | 702  | 0.4158          | 0.4024 | 0.6332  | 0.3129   |
| 0.3011        | 7.0   | 819  | 0.4176          | 0.4393 | 0.6516  | 0.3016   |
| 0.2675        | 8.0   | 936  | 0.4433          | 0.4546 | 0.6663  | 0.3290   |
| 0.2436        | 9.0   | 1053 | 0.4513          | 0.4435 | 0.6547  | 0.3258   |
| 0.2169        | 10.0  | 1170 | 0.4674          | 0.4624 | 0.6641  | 0.3177   |
| 0.2241        | 11.0  | 1287 | 0.4843          | 0.4706 | 0.6649  | 0.2984   |
| 0.1853        | 12.0  | 1404 | 0.4866          | 0.4646 | 0.6601  | 0.3323   |
| 0.1751        | 13.0  | 1521 | 0.5068          | 0.4555 | 0.6557  | 0.3081   |
| 0.1596        | 14.0  | 1638 | 0.4991          | 0.4640 | 0.6571  | 0.3145   |
| 0.1458        | 15.0  | 1755 | 0.5174          | 0.4784 | 0.6667  | 0.3210   |
| 0.1446        | 16.0  | 1872 | 0.5261          | 0.4912 | 0.6688  | 0.3065   |
| 0.1502        | 17.0  | 1989 | 0.5211          | 0.4876 | 0.6669  | 0.3242   |
| 0.1309        | 18.0  | 2106 | 0.5219          | 0.4823 | 0.6646  | 0.3242   |
| 0.1419        | 19.0  | 2223 | 0.5247          | 0.4767 | 0.6626  | 0.3258   |
| 0.138         | 20.0  | 2340 | 0.5241          | 0.4793 | 0.6631  | 0.3242   |


### Framework versions

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