# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("Juardo/my_awesome_mind_model")
model = AutoModelForAudioClassification.from_pretrained("Juardo/my_awesome_mind_model")Quick Links
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: nan
- Accuracy: 0.0796
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 | nan | 0.0796 |
| No log | 1.87 | 7 | nan | 0.0796 |
| 95.4018 | 2.93 | 11 | nan | 0.0796 |
| 95.4018 | 4.0 | 15 | nan | 0.0796 |
| 95.4018 | 4.8 | 18 | nan | 0.0796 |
| 0.0 | 5.87 | 22 | nan | 0.0796 |
| 0.0 | 6.93 | 26 | nan | 0.0796 |
| 0.0 | 8.0 | 30 | nan | 0.0796 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Juardo/my_awesome_mind_model")