forked from kubapok/en-ner-conll-2003
22 lines
615 B
Python
22 lines
615 B
Python
import pandas as pd
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import numpy as np
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import gensim
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import torch
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import pandas as pd
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import seaborn as sns
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from sklearn.model_selection import train_test_split
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# Load data
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train = pd.read_csv('train/train.tsv', sep='\t', names=['labels', 'document'])
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Y_train = train['labels'].values
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X_train = train['document'].values
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test = pd.read_csv('test-A/in.tsv', sep='\t', names=['document'])
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X_test = test['document'].values
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dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=['document'])
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exp = pd.read_csv('dev-0/expected.tsv', sep='\t', names=['labels'])
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X_dev = dev['document'].values
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Y_dev = dev['labels'].values
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