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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: 0.1896
- Accuracy: 0.96
- F1: 0.9601
- Recall: 0.96
- Precision: 0.9606
- Mcc: 0.9501
- Auc: 0.9939
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 1.5599 | 1.0 | 200 | 1.5446 | 0.415 | 0.3951 | 0.4150 | 0.6762 | 0.3213 | 0.8445 |
| 1.1707 | 2.0 | 400 | 1.0171 | 0.7575 | 0.7502 | 0.7575 | 0.7665 | 0.7023 | 0.9487 |
| 0.7857 | 3.0 | 600 | 0.7125 | 0.8375 | 0.8311 | 0.8375 | 0.8453 | 0.8008 | 0.9667 |
| 0.5713 | 4.0 | 800 | 0.5097 | 0.88 | 0.8794 | 0.8800 | 0.8929 | 0.8536 | 0.9874 |
| 0.4225 | 5.0 | 1000 | 0.3919 | 0.9075 | 0.9076 | 0.9075 | 0.9116 | 0.8853 | 0.9894 |
| 0.5846 | 6.0 | 1200 | 0.3119 | 0.9325 | 0.9327 | 0.9325 | 0.9355 | 0.9163 | 0.9883 |
| 0.3004 | 7.0 | 1400 | 0.2308 | 0.9475 | 0.9477 | 0.9475 | 0.9487 | 0.9346 | 0.9925 |
| 0.3011 | 8.0 | 1600 | 0.1974 | 0.955 | 0.9551 | 0.9550 | 0.9557 | 0.9439 | 0.9940 |
| 0.138 | 9.0 | 1800 | 0.1851 | 0.96 | 0.9601 | 0.96 | 0.9606 | 0.9501 | 0.9932 |
| 0.1582 | 10.0 | 2000 | 0.1896 | 0.96 | 0.9601 | 0.96 | 0.9606 | 0.9501 | 0.9939 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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