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---
license: bsd-3-clause
base_model: MIT/ast-finetuned-speech-commands-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ast-arabic
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. -->
# ast-arabic
This model is a fine-tuned version of [MIT/ast-finetuned-speech-commands-v2](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5734
- Accuracy: 0.8518
- Precision: 0.8598
- Recall: 0.8518
- F1: 0.8430
- Binary: 0.8957
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.19 | 50 | 4.3695 | 0.0162 | 0.0086 | 0.0162 | 0.0095 | 0.1469 |
| No log | 0.38 | 100 | 3.3975 | 0.2480 | 0.2204 | 0.2480 | 0.2000 | 0.4747 |
| No log | 0.58 | 150 | 1.9473 | 0.5067 | 0.4987 | 0.5067 | 0.4568 | 0.6574 |
| No log | 0.77 | 200 | 1.2409 | 0.6685 | 0.6872 | 0.6685 | 0.6429 | 0.7704 |
| No log | 0.96 | 250 | 0.9154 | 0.7493 | 0.7869 | 0.7493 | 0.7383 | 0.8267 |
| No log | 1.15 | 300 | 0.7568 | 0.7601 | 0.7885 | 0.7601 | 0.7487 | 0.8342 |
| No log | 1.34 | 350 | 0.6875 | 0.7951 | 0.8316 | 0.7951 | 0.7902 | 0.8558 |
| No log | 1.53 | 400 | 0.6592 | 0.8032 | 0.8336 | 0.8032 | 0.7964 | 0.8633 |
| No log | 1.73 | 450 | 0.6041 | 0.8059 | 0.8317 | 0.8059 | 0.7943 | 0.8663 |
| 1.8008 | 1.92 | 500 | 0.6096 | 0.8194 | 0.8487 | 0.8194 | 0.8087 | 0.8747 |
| 1.8008 | 2.11 | 550 | 0.5057 | 0.8410 | 0.8677 | 0.8410 | 0.8395 | 0.8887 |
| 1.8008 | 2.3 | 600 | 0.5269 | 0.8248 | 0.8642 | 0.8248 | 0.8234 | 0.8757 |
| 1.8008 | 2.49 | 650 | 0.4551 | 0.8598 | 0.8851 | 0.8598 | 0.8598 | 0.9022 |
| 1.8008 | 2.68 | 700 | 0.4962 | 0.8571 | 0.8766 | 0.8571 | 0.8552 | 0.9022 |
| 1.8008 | 2.88 | 750 | 0.5232 | 0.8464 | 0.8737 | 0.8464 | 0.8463 | 0.8935 |
| 1.8008 | 3.07 | 800 | 0.5889 | 0.8248 | 0.8583 | 0.8248 | 0.8189 | 0.8774 |
| 1.8008 | 3.26 | 850 | 0.5301 | 0.8571 | 0.8811 | 0.8571 | 0.8547 | 0.8992 |
| 1.8008 | 3.45 | 900 | 0.4731 | 0.8625 | 0.8812 | 0.8625 | 0.8614 | 0.9049 |
| 1.8008 | 3.64 | 950 | 0.4917 | 0.8544 | 0.8782 | 0.8544 | 0.8522 | 0.8992 |
| 0.2592 | 3.84 | 1000 | 0.4909 | 0.8625 | 0.8895 | 0.8625 | 0.8629 | 0.9049 |
| 0.2592 | 4.03 | 1050 | 0.5065 | 0.8544 | 0.8790 | 0.8544 | 0.8545 | 0.8992 |
| 0.2592 | 4.22 | 1100 | 0.5261 | 0.8437 | 0.8790 | 0.8437 | 0.8435 | 0.8906 |
| 0.2592 | 4.41 | 1150 | 0.5140 | 0.8598 | 0.8935 | 0.8598 | 0.8591 | 0.9019 |
| 0.2592 | 4.6 | 1200 | 0.4787 | 0.8760 | 0.8979 | 0.8760 | 0.8751 | 0.9132 |
| 0.2592 | 4.79 | 1250 | 0.5090 | 0.8652 | 0.8861 | 0.8652 | 0.8637 | 0.9049 |
| 0.2592 | 4.99 | 1300 | 0.5697 | 0.8437 | 0.8753 | 0.8437 | 0.8416 | 0.8898 |
| 0.2592 | 5.18 | 1350 | 0.5416 | 0.8625 | 0.8868 | 0.8625 | 0.8618 | 0.9038 |
| 0.2592 | 5.37 | 1400 | 0.5563 | 0.8518 | 0.8767 | 0.8518 | 0.8513 | 0.8962 |
| 0.2592 | 5.56 | 1450 | 0.5191 | 0.8679 | 0.8835 | 0.8679 | 0.8655 | 0.9097 |
| 0.0549 | 5.75 | 1500 | 0.4890 | 0.8787 | 0.9009 | 0.8787 | 0.8768 | 0.9164 |
| 0.0549 | 5.94 | 1550 | 0.4928 | 0.8814 | 0.9090 | 0.8814 | 0.8820 | 0.9181 |
| 0.0549 | 6.14 | 1600 | 0.5669 | 0.8679 | 0.8912 | 0.8679 | 0.8685 | 0.9097 |
| 0.0549 | 6.33 | 1650 | 0.5215 | 0.8760 | 0.9003 | 0.8760 | 0.8758 | 0.9132 |
| 0.0549 | 6.52 | 1700 | 0.5385 | 0.8760 | 0.9052 | 0.8760 | 0.8764 | 0.9135 |
| 0.0549 | 6.71 | 1750 | 0.5916 | 0.8625 | 0.8908 | 0.8625 | 0.8603 | 0.9038 |
| 0.0549 | 6.9 | 1800 | 0.5863 | 0.8598 | 0.8798 | 0.8598 | 0.8591 | 0.9030 |
| 0.0549 | 7.09 | 1850 | 0.5955 | 0.8571 | 0.8844 | 0.8571 | 0.8574 | 0.9011 |
| 0.0549 | 7.29 | 1900 | 0.6003 | 0.8571 | 0.8867 | 0.8571 | 0.8549 | 0.9011 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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