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neunit-changchun-20230626V1
SHENMU007/neunit-changchun-20230626V1
neunit-changchun-20230626V1 is a audio classification model from SHENMU007. Use it for the audio classification 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
- Task
- Audio Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Jun 26, 2023
- Updated
- Jun 30, 2023
- SHA
- 820b2aad
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. -->
neunit-changchun-20230626V1
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset. It achieves the following results on the evaluation set:
- Loss: 0.1940
- Accuracy: 1.0
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: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- 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: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3858 | 0.99 | 22 | 0.1940 | 1.0 |
| 0.0832 | 1.98 | 44 | 0.0460 | 1.0 |
| 0.0403 | 2.97 | 66 | 0.0285 | 1.0 |
| 0.0297 | 4.0 | 89 | 0.0227 | 1.0 |
| 0.025 | 4.94 | 110 | 0.0213 | 1.0 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Audio Classification
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Jun 26, 2023
- Updated
- Jun 30, 2023