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---
base_model: microsoft/unispeech-sat-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: unispeech-sat-base-finetuned-common_voice
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. -->
# unispeech-sat-base-finetuned-common_voice
This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co./microsoft/unispeech-sat-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0641
- Accuracy: 0.9875
- F1: 0.9875
- Recall: 0.9875
- Precision: 0.9878
- Mcc: 0.9844
- Auc: 0.9999
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 0.2181 | 1.0 | 200 | 0.0440 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 0.9981 |
| 0.0083 | 2.0 | 400 | 0.0609 | 0.9875 | 0.9875 | 0.9875 | 0.9880 | 0.9845 | 0.9987 |
| 0.0035 | 3.0 | 600 | 0.0888 | 0.98 | 0.9799 | 0.9800 | 0.9806 | 0.9752 | 0.9991 |
| 0.2407 | 4.0 | 800 | 0.1593 | 0.9725 | 0.9726 | 0.9725 | 0.9740 | 0.9660 | 0.9997 |
| 0.0859 | 5.0 | 1000 | 0.1234 | 0.9775 | 0.9777 | 0.9775 | 0.9790 | 0.9722 | 0.9999 |
| 0.2073 | 6.0 | 1200 | 0.0851 | 0.9825 | 0.9826 | 0.9825 | 0.9832 | 0.9783 | 0.9999 |
| 0.0036 | 7.0 | 1400 | 0.0550 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9999 |
| 0.0036 | 8.0 | 1600 | 0.0600 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 1.0000 |
| 0.0013 | 9.0 | 1800 | 0.0645 | 0.99 | 0.9900 | 0.99 | 0.9903 | 0.9876 | 1.0000 |
| 0.0048 | 10.0 | 2000 | 0.0641 | 0.9875 | 0.9875 | 0.9875 | 0.9878 | 0.9844 | 0.9999 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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