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---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: hubert-base-ls960-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. -->
# hubert-base-ls960-finetuned-common_voice
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2002
- Accuracy: 0.955
- F1: 0.9549
- Recall: 0.9550
- Precision: 0.9551
- Mcc: 0.9438
- Auc: 0.9942
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 1.5544 | 1.0 | 200 | 1.5193 | 0.405 | 0.3628 | 0.4050 | 0.5940 | 0.2904 | 0.8407 |
| 1.1406 | 2.0 | 400 | 0.9811 | 0.6375 | 0.5780 | 0.6375 | 0.6712 | 0.5734 | 0.9464 |
| 0.7902 | 3.0 | 600 | 0.6775 | 0.8125 | 0.7969 | 0.8125 | 0.8181 | 0.7740 | 0.9724 |
| 0.5346 | 4.0 | 800 | 0.5083 | 0.8725 | 0.8683 | 0.8725 | 0.8774 | 0.8438 | 0.9834 |
| 0.5139 | 5.0 | 1000 | 0.3943 | 0.9025 | 0.8988 | 0.9025 | 0.9074 | 0.8809 | 0.9879 |
| 0.5136 | 6.0 | 1200 | 0.3314 | 0.915 | 0.9145 | 0.915 | 0.9174 | 0.8945 | 0.9881 |
| 0.3726 | 7.0 | 1400 | 0.2894 | 0.925 | 0.9241 | 0.925 | 0.9258 | 0.9069 | 0.9878 |
| 0.3072 | 8.0 | 1600 | 0.2267 | 0.9325 | 0.9314 | 0.9325 | 0.9349 | 0.9167 | 0.9914 |
| 0.1948 | 9.0 | 1800 | 0.2117 | 0.945 | 0.9445 | 0.945 | 0.9461 | 0.9317 | 0.9931 |
| 0.2312 | 10.0 | 2000 | 0.2002 | 0.955 | 0.9549 | 0.9550 | 0.9551 | 0.9438 | 0.9942 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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