Infinity Future infinityfuture
infinityfuture
/ fasttext_gensim.py
Created
February 6, 2019 14:50
Using Gensim train FastText Chinese Char vector
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| import pickle | |
| from gensim.models.fasttext import FastText | |
| # [ ['你', '好', '吗'] ] | |
| sentences = pickle.load(open('./x.pkl', 'rb')) | |
| model = FastText(size=64, window=10, min_count=1) | |
| model.build_vocab(sentences=sentences) | |
| model.train(sentences=sentences, total_examples=len(sentences), epochs=10) | |
| model.wv.save_word2vec_format('./fasttext64.txt') |
infinityfuture
/ textrank_summarization_word2vec.py
Created
October 4, 2018 08:11
Use TextRank algorithm to generate summary, using word2vec as sentence similarity
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| """ | |
| Reference: | |
| http://www.hankcs.com/nlp/textrank-algorithm-to-extract-the-keywords-java-implementation.html | |
| http://www.hankcs.com/nlp/textrank-algorithm-java-implementation-of-automatic-abstract.html | |
| Chinese Embedding From | |
| https://github.com/Embedding/Chinese-Word-Vectors |
infinityfuture
/ textrank_keywords_word2vec.py
Created
October 4, 2018 08:08
TextRank extract keywords using word2vec as similarity
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| """ | |
| Reference: | |
| http://www.hankcs.com/nlp/textrank-algorithm-to-extract-the-keywords-java-implementation.html | |
| http://www.hankcs.com/nlp/textrank-algorithm-java-implementation-of-automatic-abstract.html | |
| Chinese Embedding From | |
| https://github.com/Embedding/Chinese-Word-Vectors |
infinityfuture
/ textrank_summarization.py
Last active
October 4, 2018 08:11
Use TextRank algorithm to generate summary
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| """ | |
| Reference: | |
| http://www.hankcs.com/nlp/textrank-algorithm-to-extract-the-keywords-java-implementation.html | |
| http://www.hankcs.com/nlp/textrank-algorithm-java-implementation-of-automatic-abstract.html | |
| """ | |
| from gensim.summarization.bm25 import get_bm25_weights | |
| import numpy as np | |
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| """ | |
| Reference: | |
| http://www.hankcs.com/nlp/textrank-algorithm-to-extract-the-keywords-java-implementation.html | |
| http://www.hankcs.com/nlp/textrank-algorithm-java-implementation-of-automatic-abstract.html | |
| """ | |
| import numpy as np |
infinityfuture
/ pytorch_global_max_pooling.py
Last active
October 3, 2018 16:47
Pytorch Global Max Pooling
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| def global_max_pooling(tensor, dim, topk): | |
| """Global max pooling""" | |
| ret, _ = torch.topk(tensor, topk, dim) | |
| return ret |