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---
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: 0.7102
- Accuracy: 0.7585
- Precision: 0.7838
- Recall: 0.6493
- F1: 0.7102
- Precision Neutral: 0.7432
- Recall Neutral: 0.85
- F1 Neutral: 0.7930
- Precision Anger: 0.7838
- Recall Anger: 0.6493
- F1 Anger: 0.7102
## 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: 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: cosine
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|
| 0.6917 | 1.0 | 294 | 0.6704 | 0.5578 | 0.5078 | 0.9701 | 0.6667 | 0.8947 | 0.2125 | 0.3434 | 0.5078 | 0.9701 | 0.6667 |
| 0.6453 | 2.0 | 588 | 0.6077 | 0.7007 | 0.8194 | 0.4403 | 0.5728 | 0.6622 | 0.9187 | 0.7696 | 0.8194 | 0.4403 | 0.5728 |
| 0.5272 | 3.0 | 882 | 0.7842 | 0.6633 | 0.9487 | 0.2761 | 0.4277 | 0.6196 | 0.9875 | 0.7614 | 0.9487 | 0.2761 | 0.4277 |
| 0.4118 | 4.0 | 1176 | 0.6566 | 0.7449 | 0.736 | 0.6866 | 0.7104 | 0.7515 | 0.7937 | 0.7720 | 0.736 | 0.6866 | 0.7104 |
| 0.4956 | 5.0 | 1470 | 0.7102 | 0.7585 | 0.7838 | 0.6493 | 0.7102 | 0.7432 | 0.85 | 0.7930 | 0.7838 | 0.6493 | 0.7102 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 2.18.0
- Tokenizers 0.21.0
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