Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use XingLin900324/my_awesome_mind_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XingLin900324/my_awesome_mind_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="XingLin900324/my_awesome_mind_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("XingLin900324/my_awesome_mind_model") model = AutoModelForAudioClassification.from_pretrained("XingLin900324/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: 2.6514
- Accuracy: 0.0708
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 | 2.6413 | 0.0708 |
| No log | 1.8667 | 7 | 2.6400 | 0.0708 |
| 2.6366 | 2.9333 | 11 | 2.6446 | 0.0796 |
| 2.6366 | 4.0 | 15 | 2.6477 | 0.0885 |
| 2.6366 | 4.8 | 18 | 2.6500 | 0.0708 |
| 2.6277 | 5.8667 | 22 | 2.6507 | 0.0708 |
| 2.6277 | 6.9333 | 26 | 2.6514 | 0.0708 |
| 2.6205 | 8.0 | 30 | 2.6514 | 0.0708 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for XingLin900324/my_awesome_mind_model
Base model
facebook/wav2vec2-baseEvaluation results
- Accuracy on minds14self-reported0.071