Testing_for_EEG – AI Model by Masataro | AlphaNeural AI

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Masataro/
Testing_for_EEG

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audio-classification

generated_from_trainer

facebook/data2vec-audio-base

endpoints_compatible

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Testing_for_EEG

This model is a fine-tuned version of facebook/data2vec-audio-base on the minds14 dataset. It achieves the following results on the evaluation set:

Loss: 2.6516
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	2.6381	0.0796
No log	1.8667	7	2.6388	0.0796
2.6382	2.9333	11	2.6407	0.0796
2.6382	4.0	15	2.6433	0.0973
2.6382	4.8	18	2.6489	0.0796
2.6336	5.8667	22	2.6496	0.0796
2.6336	6.9333	26	2.6501	0.0796
2.6384	8.0	30	2.6516	0.0796

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1