Instructions to use mazesmazes/tiny-audio-next-thurs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mazesmazes/tiny-audio-next-thurs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mazesmazes/tiny-audio-next-thurs", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mazesmazes/tiny-audio-next-thurs", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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library_name: transformers
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### Direct Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Factors
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### Results
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#### Summary
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## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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## Glossary [optional]
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## More Information [optional]
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---
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library_name: transformers
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tags:
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- generated_from_trainer
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model-index:
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- name: tiny-audio-next-thurs
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results: []
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# tiny-audio-next-thurs
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This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3428
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.001
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- train_batch_size: 100
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- eval_batch_size: 100
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- seed: 43
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine_with_min_lr
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:-----:|:---------------:|
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| 0.2913 | 0.0450 | 2000 | 0.4665 |
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| 0.2560 | 0.0900 | 4000 | 0.4262 |
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| 0.2500 | 0.1350 | 6000 | 0.4156 |
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| 0.2387 | 0.1800 | 8000 | 0.4142 |
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| 0.2258 | 0.2250 | 10000 | 0.3964 |
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| 0.2220 | 0.2700 | 12000 | 0.3896 |
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| 0.2183 | 0.3150 | 14000 | 0.3913 |
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| 0.2112 | 0.3600 | 16000 | 0.3841 |
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| 0.2086 | 0.4050 | 18000 | 0.3763 |
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| 0.2042 | 0.4501 | 20000 | 0.3732 |
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| 0.1944 | 0.4951 | 22000 | 0.3659 |
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| 0.1893 | 0.5401 | 24000 | 0.3631 |
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| 0.1942 | 0.5851 | 26000 | 0.3589 |
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| 0.1861 | 0.6301 | 28000 | 0.3567 |
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| 0.1894 | 0.6751 | 30000 | 0.3515 |
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| 0.1807 | 0.7201 | 32000 | 0.3497 |
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| 0.1794 | 0.7651 | 34000 | 0.3456 |
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| 0.1745 | 0.8101 | 36000 | 0.3453 |
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| 0.1704 | 0.8551 | 38000 | 0.3459 |
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| 0.1754 | 0.9001 | 40000 | 0.3446 |
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| 0.1735 | 0.9451 | 42000 | 0.3440 |
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| 0.1737 | 0.9901 | 44000 | 0.3415 |
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| 0.1755 | 1.0 | 44439 | 0.3428 |
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### Framework versions
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- Transformers 5.7.0
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- Pytorch 2.8.0+cu128
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- Datasets 3.6.0
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- Tokenizers 0.22.2
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