Instructions to use binmacn/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binmacn/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="binmacn/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("binmacn/results") model = AutoModelForAudioClassification.from_pretrained("binmacn/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from binmacn/results: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/binmacn/results/resolve/main/README.md
- Command line
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hf download hf://binmacn/results/README.md
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curl -L -o README.md https://huggingface.co/binmacn/results/resolve/main/README.md
1.56 kB
| license: bsd-3-clause | |
| base_model: MIT/ast-finetuned-audioset-10-10-0.4593 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: results | |
| 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. --> | |
| # results | |
| This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2118 | |
| - Accuracy: 0.9699 | |
| ## 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: 5e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.7356 | 1.0 | 631 | 0.3865 | 0.9209 | | |
| | 0.1865 | 2.0 | 1262 | 0.2016 | 0.9557 | | |
| | 0.0 | 3.0 | 1893 | 0.2118 | 0.9699 | | |
| ### Framework versions | |
| - Transformers 4.39.2 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |