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