otthorn/TP_WordEmbedding
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higepi 2022-10-06 16:49:46 +02:00
commit 0ed986773b
3 changed files with 12 additions and 1 deletions
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Word_embedding.docx
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9
wordvec.ipynb
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@ -255,6 +255,15 @@
"source": [ "source": [
"print(wv.doesnt_match(['wood', 'oak', 'tree', 'iron', 'leaf']))" "print(wv.doesnt_match(['wood', 'oak', 'tree', 'iron', 'leaf']))"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(wv.most_similar(positive=['The largest country is']))"
]
} }
], ],
"metadata": { "metadata": {

4
wordvec.py
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@ -15,4 +15,6 @@ vec_man = wv['man']
vec_woman = wv['woman'] vec_woman = wv['woman']
result = wv.most_similar(positive=(vec_father - vec_man + vec_woman), topn=1) result = wv.most_similar(positive=(vec_father - vec_man + vec_woman), topn=1)
print(result) print(result)
print(wv.most_similar(positive=['The largest country is']))