---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: deberta-emotion-recognition
  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. -->

# deberta-emotion-recognition

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5153
- Accuracy: 0.9325
- F1: 0.9075

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4726        | 1.0   | 1000  | 0.4306          | 0.8585   | 0.8298 |
| 0.237         | 2.0   | 2000  | 0.2473          | 0.927    | 0.9025 |
| 0.2206        | 3.0   | 3000  | 0.1694          | 0.9365   | 0.9126 |
| 0.1654        | 4.0   | 4000  | 0.2024          | 0.934    | 0.9134 |
| 0.1138        | 5.0   | 5000  | 0.2417          | 0.9325   | 0.9082 |
| 0.4768        | 6.0   | 6000  | 0.2855          | 0.9385   | 0.9169 |
| 0.0168        | 7.0   | 7000  | 0.4197          | 0.9385   | 0.9169 |
| 0.0121        | 8.0   | 8000  | 0.4521          | 0.934    | 0.9119 |
| 0.1931        | 9.0   | 9000  | 0.5252          | 0.9335   | 0.9095 |
| 0.0221        | 10.0  | 10000 | 0.5046          | 0.9395   | 0.9160 |
| 0.0112        | 11.0  | 11000 | 0.5153          | 0.9325   | 0.9075 |


### Framework versions

- Transformers 4.57.2
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2