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---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- Emo-Codec/CREMA-D_synth
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hubert-base-ls960-tone-classification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: CREMA-D
type: Emo-Codec/CREMA-D_synth
metrics:
- name: Accuracy
type: accuracy
value: 0.8016085790884718
- name: Precision
type: precision
value: 0.8014677098753149
- name: Recall
type: recall
value: 0.8016085790884718
- name: F1
type: f1
value: 0.7989608760238184
---
<!-- 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-base-ls960-tone-classification
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the CREMA-D dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7499
- Accuracy: 0.8016
- Precision: 0.8015
- Recall: 0.8016
- F1: 0.7990
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.4326 | 1.0 | 442 | 1.2934 | 0.5147 | 0.5889 | 0.5147 | 0.4878 |
| 1.0447 | 2.0 | 884 | 0.8590 | 0.7051 | 0.7570 | 0.7051 | 0.7125 |
| 0.775 | 3.0 | 1326 | 0.7668 | 0.7426 | 0.7589 | 0.7426 | 0.7404 |
| 0.6593 | 4.0 | 1768 | 0.8127 | 0.7265 | 0.7564 | 0.7265 | 0.7245 |
| 0.5014 | 5.0 | 2210 | 0.8670 | 0.7507 | 0.7631 | 0.7507 | 0.7436 |
| 0.48 | 6.0 | 2652 | 0.7473 | 0.7694 | 0.7739 | 0.7694 | 0.7623 |
| 0.3505 | 7.0 | 3094 | 0.7647 | 0.8016 | 0.8039 | 0.8016 | 0.7991 |
| 0.3223 | 8.0 | 3536 | 0.7499 | 0.8016 | 0.8015 | 0.8016 | 0.7990 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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