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program.py
11
program.py
@ -5,7 +5,7 @@ from sklearn import preprocessing
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import pandas as pd
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from gensim.test.utils import common_texts
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from gensim.models import Word2Vec
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00
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with open("train/in.tsv") as f:
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X_train = f.readlines()
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with open("train/expected.tsv") as ff:
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@ -13,10 +13,11 @@ with open("train/in.tsv") as f:
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with open("test-A/in.tsv") as d:
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X = d.readlines()
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model = Word2Vec(X_train, min_count=1,size= 50,workers=3, window =3, sg = 1)
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X_train=model[X_train]
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X=model[X]
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FEAUTERES=1000
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model = Word2Vec(X_train, min_count=1,size= 500,workers=3, window =3, sg = 1)
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model1 = Word2Vec(X, min_count=1,size= 500,workers=3, window =3, sg = 1)
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X_train=model.wv
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X=model1.wv
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FEAUTERES=500
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class NeuralNetworkModel(torch.nn.Module):
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