Text Classification
Transformers
Safetensors
PyTorch
ctnet
eeg
brain-computer-interface
motor-imagery
bnci2014-001
ct-net
custom-code
custom_code
Instructions to use likan-blk/ctnet-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use likan-blk/ctnet-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="likan-blk/ctnet-hf", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("likan-blk/ctnet-hf", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoFeatureExtractor": "preprocessing.CtnetPreprocessor" | |
| }, | |
| "channel_names": [ | |
| "Fz", | |
| "FC3", | |
| "FC1", | |
| "FCz", | |
| "FC2", | |
| "FC4", | |
| "C5", | |
| "C3", | |
| "C1", | |
| "Cz", | |
| "C2", | |
| "C4", | |
| "C6", | |
| "CP3", | |
| "CP1", | |
| "CPz", | |
| "CP2", | |
| "CP4", | |
| "P1", | |
| "Pz", | |
| "P2", | |
| "POz" | |
| ], | |
| "dataset": "BNCI2014_001", | |
| "feature_extractor_type": "CtnetPreprocessor", | |
| "mean": 0.3287310004234314, | |
| "n_channels": 22, | |
| "n_times": 1000, | |
| "sampling_rate": 250, | |
| "selection_session": "1test", | |
| "std": 10.960787773132324, | |
| "subjects": [ | |
| 1, | |
| 2, | |
| 3, | |
| 4, | |
| 5, | |
| 6, | |
| 7, | |
| 8, | |
| 9 | |
| ], | |
| "train_session": "0train", | |
| "unit": "microvolts" | |
| } | |