Instructions to use marcatanante1/my_awesome_mind_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcatanante1/my_awesome_mind_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="marcatanante1/my_awesome_mind_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("marcatanante1/my_awesome_mind_model") model = AutoModelForAudioClassification.from_pretrained("marcatanante1/my_awesome_mind_model") - Notebooks
- Google Colab
- Kaggle
my_awesome_mind_model
This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 2.6773
- Accuracy: 0.0177
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: 42
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.8 | 3 | 2.6420 | 0.0531 |
| No log | 1.87 | 7 | 2.6416 | 0.0708 |
| 2.6354 | 2.93 | 11 | 2.6564 | 0.0708 |
| 2.6354 | 4.0 | 15 | 2.6724 | 0.0265 |
| 2.6354 | 4.8 | 18 | 2.6769 | 0.0265 |
| 2.6236 | 5.87 | 22 | 2.6782 | 0.0265 |
| 2.6236 | 6.93 | 26 | 2.6772 | 0.0177 |
| 2.6115 | 8.0 | 30 | 2.6773 | 0.0177 |
Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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