---
library_name: peft
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: emotion-model11_0
  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_0

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: 0.9531
- Accuracy: 0.6135
- F1: 0.5573

## 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   | 1.3368          | 0.3804   | 0.2096 |
| 1.3413        | 2.0   | 82   | 1.2883          | 0.3804   | 0.2096 |
| 1.2917        | 3.0   | 123  | 1.0577          | 0.5521   | 0.4841 |
| 1.1702        | 4.0   | 164  | 1.0247          | 0.5337   | 0.4033 |
| 1.1099        | 5.0   | 205  | 0.9804          | 0.5460   | 0.4324 |
| 1.1099        | 6.0   | 246  | 0.9531          | 0.6135   | 0.5573 |
| 1.0856        | 7.0   | 287  | 0.9336          | 0.6135   | 0.5301 |
| 1.0752        | 8.0   | 328  | 0.9257          | 0.5767   | 0.4883 |
| 1.0393        | 9.0   | 369  | 0.9182          | 0.5828   | 0.5188 |
| 1.0449        | 10.0  | 410  | 0.9131          | 0.5828   | 0.5188 |


### Framework versions

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