---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: emotion_roberta_weighted
  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_roberta_weighted

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2084
- Accuracy: 0.9315
- Precision: 0.9379
- Recall: 0.9315
- F1: 0.9331

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3177        | 1.0   | 1000 | 0.2423          | 0.918    | 0.9245    | 0.918  | 0.9195 |
| 0.1804        | 2.0   | 2000 | 0.1776          | 0.931    | 0.9366    | 0.931  | 0.9317 |
| 0.1504        | 3.0   | 3000 | 0.1740          | 0.935    | 0.9410    | 0.935  | 0.9362 |
| 0.1203        | 4.0   | 4000 | 0.1723          | 0.9405   | 0.9438    | 0.9405 | 0.9413 |
| 0.0889        | 5.0   | 5000 | 0.2068          | 0.939    | 0.9420    | 0.939  | 0.9398 |


### Framework versions

- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1