---
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: deberta-v3-xsmall-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.932
---

<!-- 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. -->

# deberta-v3-xsmall-emotion

This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1877
- Accuracy: 0.932

## 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: 3e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3683        | 1.0   | 500  | 0.8479          | 0.6975   |
| 0.547         | 2.0   | 1000 | 0.2881          | 0.905    |
| 0.2378        | 3.0   | 1500 | 0.2116          | 0.925    |
| 0.1704        | 4.0   | 2000 | 0.1877          | 0.932    |
| 0.1392        | 5.0   | 2500 | 0.1718          | 0.9295   |


### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3