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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: 0.4616
- Accuracy: 0.9375
- F1: 0.9377
- Recall: 0.9375
- Precision: 0.9403
- Mcc: 0.9225
- Auc: 0.9925
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 1.601 | 0.96 | 12 | 1.5594 | 0.385 | 0.3217 | 0.3850 | 0.6064 | 0.2666 | 0.7895 |
| 1.5467 | 2.0 | 25 | 1.3344 | 0.67 | 0.6516 | 0.6700 | 0.7185 | 0.6030 | 0.9009 |
| 1.4062 | 2.96 | 37 | 1.0521 | 0.8 | 0.7964 | 0.8 | 0.8014 | 0.7521 | 0.9436 |
| 1.0881 | 4.0 | 50 | 0.8340 | 0.8525 | 0.8502 | 0.8525 | 0.8677 | 0.8201 | 0.9759 |
| 0.9348 | 4.96 | 62 | 0.7227 | 0.89 | 0.8894 | 0.89 | 0.8939 | 0.8639 | 0.9801 |
| 0.8596 | 6.0 | 75 | 0.5873 | 0.9275 | 0.9276 | 0.9275 | 0.9300 | 0.9100 | 0.9908 |
| 0.7917 | 6.96 | 87 | 0.5208 | 0.93 | 0.9298 | 0.93 | 0.9310 | 0.9128 | 0.9940 |
| 0.6721 | 8.0 | 100 | 0.4784 | 0.9475 | 0.9476 | 0.9475 | 0.9491 | 0.9348 | 0.9935 |
| 0.6297 | 8.96 | 112 | 0.4734 | 0.9325 | 0.9326 | 0.9325 | 0.9363 | 0.9166 | 0.9916 |
| 0.6127 | 9.6 | 120 | 0.4616 | 0.9375 | 0.9377 | 0.9375 | 0.9403 | 0.9225 | 0.9925 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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