---
library_name: peft
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: emotion-model11_3
  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. -->

# emotion-model11_3

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: 0.9797
- Accuracy: 0.6196
- F1: 0.5679

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 41   | 1.2919          | 0.4724   | 0.3031 |
| 1.3606        | 2.0   | 82   | 1.2506          | 0.4724   | 0.3070 |
| 1.3078        | 3.0   | 123  | 1.1353          | 0.4724   | 0.4184 |
| 1.1868        | 4.0   | 164  | 1.1391          | 0.5092   | 0.4586 |
| 1.1112        | 5.0   | 205  | 1.0539          | 0.5644   | 0.4994 |
| 1.1112        | 6.0   | 246  | 1.0078          | 0.6135   | 0.5637 |
| 1.1175        | 7.0   | 287  | 1.0021          | 0.6135   | 0.5666 |
| 1.0865        | 8.0   | 328  | 0.9713          | 0.6012   | 0.5337 |
| 1.0848        | 9.0   | 369  | 0.9830          | 0.6074   | 0.5492 |
| 1.0823        | 10.0  | 410  | 0.9797          | 0.6196   | 0.5679 |


### Framework versions

- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1