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nonexperts_binary_wav2vec2-large-xls-r-300m-dm32
dpraitality/nonexperts_binary_wav2vec2-large-xls-r-300m-dm32
nonexperts_binary_wav2vec2-large-xls-r-300m-dm32 is a machine learning model from dpraitality. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Dec 8, 2025
- Updated
- Dec 8, 2025
- SHA
- b2a1eff4
Base models
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. -->
nonexperts_binary_wav2vec2-large-xls-r-300m-dm32
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0544
- Accuracy: 0.6383
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 22
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 34 | 0.6083 | 0.7021 |
| No log | 2.0 | 68 | 0.6093 | 0.7021 |
| No log | 3.0 | 102 | 0.6071 | 0.7021 |
| 0.6221 | 4.0 | 136 | 0.6083 | 0.7021 |
| 0.6221 | 5.0 | 170 | 0.6070 | 0.7021 |
| 0.6221 | 6.0 | 204 | 0.6239 | 0.7021 |
| 0.6065 | 7.0 | 238 | 0.6066 | 0.7021 |
| 0.6065 | 8.0 | 272 | 0.6088 | 0.7021 |
| 0.6065 | 9.0 | 306 | 0.6243 | 0.7021 |
| 0.6026 | 10.0 | 340 | 0.6300 | 0.7021 |
| 0.6026 | 11.0 | 374 | 0.6385 | 0.7021 |
| 0.6026 | 12.0 | 408 | 0.6292 | 0.7021 |
| 0.5726 | 13.0 | 442 | 0.7407 | 0.7128 |
| 0.5726 | 14.0 | 476 | 0.7043 | 0.6383 |
| 0.5726 | 15.0 | 510 | 0.7099 | 0.6489 |
| 0.5726 | 16.0 | 544 | 0.7087 | 0.6915 |
| 0.4798 | 17.0 | 578 | 0.7841 | 0.6809 |
| 0.4798 | 18.0 | 612 | 1.0283 | 0.5638 |
| 0.4798 | 19.0 | 646 | 0.8606 | 0.6702 |
| 0.3346 | 20.0 | 680 | 0.9724 | 0.6489 |
| 0.3346 | 21.0 | 714 | 1.0003 | 0.6596 |
| 0.3346 | 22.0 | 748 | 1.0544 | 0.6383 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Dec 8, 2025
- Updated
- Dec 8, 2025