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int64 0
2.57k
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class label 3
classes | label
class label 3
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0 | 0No Event
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1 | 0No Event
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2 | 0No Event
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3 | 0No Event
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4 | 0No Event
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5 | 0No Event
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6 | 0No Event
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7 | 0No Event
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8 | 2seiz
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9 | 2seiz
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10 | 2seiz
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11 | 2seiz
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57 | 2seiz
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58 | 2seiz
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59 | 2seiz
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60 | 0No Event
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61 | 0No Event
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62 | 0No Event
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63 | 0No Event
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64 | 0No Event
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65 | 0No Event
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66 | 0No Event
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67 | 0No Event
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68 | 0No Event
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69 | 0No Event
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70 | 0No Event
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71 | 0No Event
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72 | 0No Event
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73 | 0No Event
| 0No Event
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0 | 0No Event
| 0No Event
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1 | 0No Event
| 0No Event
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2 | 2seiz
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3 | 2seiz
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4 | 2seiz
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5 | 2seiz
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24 | 2seiz
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25 | 2seiz
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Dataset Card for "seizure_eeg_train"
from datasets import load_dataset
dataset_name = "JLB-JLB/seizure_eeg_train"
dataset = load_dataset(
dataset_name,
split="train",
)
display(dataset)
# create train and test/val split
train_testvalid = dataset.train_test_split(test_size=0.1, shuffle=True, seed=12071998)
display(train_testvalid)
# get the number of different labels in the train, test and validation set
display(train_testvalid["train"].features["label"])
display(train_testvalid["test"].features["label"].num_classes)
# check how many labels/number of classes
num_classes = len(set(train_testvalid["train"]['label']))
labels = train_testvalid["train"].features['label']
print(um_classes, labels)
display(train_testvalid["train"][0]['image'])
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