---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: roberta-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.938
---

<!-- 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. -->

# roberta-emotion

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1394
- Accuracy: 0.938

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6103        | 1.0   | 500  | 0.2516          | 0.9195   |
| 0.1981        | 2.0   | 1000 | 0.1747          | 0.9345   |
| 0.1214        | 3.0   | 1500 | 0.1394          | 0.938    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0