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ft-wav2vec2-with-minds
ALBADDAWI/ft-wav2vec2-with-minds
ft-wav2vec2-with-minds is a machine learning model from ALBADDAWI. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Model type
- wav2vec2
- Access
- Public
- Created
- Aug 29, 2024
- Updated
- Aug 31, 2024
- SHA
- 1cebb6a9
Model card
From the Hugging Face model README
<!-- 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. -->
ft-wav2vec2-with-minds
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0333
- Accuracy: 0.9972
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: 120
- eval_batch_size: 120
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 480
- 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 |
|---|---|---|---|---|
| 4.6092 | 1.0 | 9 | 2.4860 | 0.4311 |
| 1.4641 | 2.0 | 18 | 0.5758 | 0.7826 |
| 0.5061 | 3.0 | 27 | 0.1966 | 0.9756 |
| 0.2573 | 4.0 | 36 | 0.1038 | 0.9803 |
| 0.1557 | 5.0 | 45 | 0.0671 | 0.9859 |
| 0.1235 | 6.0 | 54 | 0.0333 | 0.9972 |
| 0.0725 | 7.0 | 63 | 0.0334 | 0.9944 |
| 0.0914 | 8.0 | 72 | 0.0279 | 0.9953 |
| 0.1695 | 9.0 | 81 | 0.0276 | 0.9972 |
| 0.1118 | 10.0 | 90 | 0.0290 | 0.9972 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.12.1+cu116
- Datasets 2.15.0
- Tokenizers 0.15.2
- Type
- wav2vec2
- Created
- Aug 29, 2024
- Updated
- Aug 31, 2024