Instructions to use yyw2683/my_awesome_mind_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yyw2683/my_awesome_mind_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="yyw2683/my_awesome_mind_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("yyw2683/my_awesome_mind_model") model = AutoModelForAudioClassification.from_pretrained("yyw2683/my_awesome_mind_model") - Notebooks
- Google Colab
- Kaggle
my_awesome_mind_model
This model is a fine-tuned version of facebook/hubert-large-ls960-ft on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6493
- Accuracy: 0.6923
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9231 | 3 | 0.6925 | 0.5577 |
| No log | 1.8462 | 6 | 0.6674 | 0.6923 |
| No log | 2.7692 | 9 | 0.6542 | 0.6923 |
| 0.6693 | 3.6923 | 12 | 0.6493 | 0.6923 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for yyw2683/my_awesome_mind_model
Base model
facebook/hubert-large-ls960-ft