---
library_name: transformers
license: apache-2.0
base_model: m3hrdadfi/hubert-base-persian-speech-emotion-recognition
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Hubert-fine-tuned-persian
  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. -->

# Hubert-fine-tuned-persian

This model is a fine-tuned version of [m3hrdadfi/hubert-base-persian-speech-emotion-recognition](https://huggingface.co/m3hrdadfi/hubert-base-persian-speech-emotion-recognition) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5445
- Accuracy: 0.7483
- Precision: 0.7
- Recall: 0.7836
- F1: 0.7394
- Precision Neutral: 0.7986
- Recall Neutral: 0.7188
- F1 Neutral: 0.7566
- Precision Anger: 0.7
- Recall Anger: 0.7836
- F1 Anger: 0.7394

## 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: 2.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Precision Neutral | Recall Neutral | F1 Neutral | Precision Anger | Recall Anger | F1 Anger |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|
| 0.7337        | 1.0   | 294  | 0.6602          | 0.5816   | 0.5216    | 0.9925 | 0.6838 | 0.9744            | 0.2375         | 0.3819     | 0.5216          | 0.9925       | 0.6838   |
| 0.5922        | 2.0   | 588  | 0.5445          | 0.7483   | 0.7       | 0.7836 | 0.7394 | 0.7986            | 0.7188         | 0.7566     | 0.7             | 0.7836       | 0.7394   |
| 0.4774        | 3.0   | 882  | 0.7353          | 0.7177   | 0.7257    | 0.6119 | 0.6640 | 0.7127            | 0.8063         | 0.7566     | 0.7257          | 0.6119       | 0.6640   |
| 0.4527        | 4.0   | 1176 | 0.6275          | 0.7143   | 0.6582    | 0.7761 | 0.7123 | 0.7794            | 0.6625         | 0.7162     | 0.6582          | 0.7761       | 0.7123   |
| 0.5922        | 5.0   | 1470 | 0.8464          | 0.7347   | 0.6647    | 0.8433 | 0.7434 | 0.8306            | 0.6438         | 0.7254     | 0.6647          | 0.8433       | 0.7434   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 2.18.0
- Tokenizers 0.21.0