Instructions to use Anguuuuus/mydata-iau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anguuuuus/mydata-iau with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Anguuuuus/mydata-iau")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Anguuuuus/mydata-iau") model = AutoModelForAudioClassification.from_pretrained("Anguuuuus/mydata-iau", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
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README.md
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6266
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- Accuracy: 0.5385
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.7031 | 1.0 | 4 | 0.6858 | 0.5385 |
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| 0.631 | 2.0 | 8 | 0.6973 | 0.3846 |
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| 0.7272 | 3.0 | 12 | 0.7615 | 0.3846 |
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| 0.5345 | 4.0 | 16 | 0.7592 | 0.4615 |
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| 0.7417 | 5.0 | 20 | 0.7341 | 0.4615 |
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| 0.6045 | 6.0 | 24 | 0.7095 | 0.4615 |
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| 0.5344 | 7.0 | 28 | 0.7080 | 0.3846 |
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| 0.4422 | 8.0 | 32 | 0.7393 | 0.4615 |
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| 0.4156 | 9.0 | 36 | 0.7389 | 0.4615 |
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| 0.3876 | 10.0 | 40 | 0.7068 | 0.5385 |
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| 0.3761 | 11.0 | 44 | 0.6788 | 0.5385 |
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| 0.433 | 12.0 | 48 | 0.6340 | 0.5385 |
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| 0.3416 | 13.0 | 52 | 0.6109 | 0.5385 |
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| 0.3447 | 14.0 | 56 | 0.6167 | 0.5385 |
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| 0.3328 | 15.0 | 60 | 0.6266 | 0.5385 |
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### Framework versions
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model.safetensors
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training_args.bin
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