Topic modeling
51 USDUse data from https://github.com/thedenaas/hse_seminars/tree/master/2018/seminar_13/data.zip
Implement model in pytorch from "An Unsupervised Neural Attention Model for Aspect Extraction, He et al, 2017", also desribed in https://github.com/thedenaas/hse_seminars/blob/master/2019/seminar_13/topic.ipynb .
You can use sentence embeddings with attention :
sentence embedding
attention weight for i-th token
attention with trainable matrix 
sentence context
, token embedding of size d
- number of tokens in a sentence
Or just use sentence embedding as an average over word embeddings :
sentence embedding, token embedding of size d
- number of tokens in a sentence
topic weights for sentence 
, with trainable matrix 
and bias vector 
reconstructed sentence embedding as a weighted sum of topic embeddings
trainable matrix of topic embeddings, K=number of topics
Training objective:
where
random sentences are sampled as negative examples from dataset 
for each sentence 
average of word embeddings in the i-th sentence
regularizer, that enforces matrix 
to be orthogonal
Frobenius norm
Compute topic coherence for at least for 3 different number of topics. Use 10 nearest words for each topic. It means you have to train one model for each number of topics. You can use code from seminar notes with word2vec similarity scores.
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2 days56 USD
80 2 days56 USD
Могу выполнить Ваше задание. Есть опыт работы с нейронными сетями и имплементации их с научных статей на pytorch/tensorflow.
О сроках и цене можем договориться.
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7 days180 USD
356 8 0 7 days180 USD
Добрый день!
У меня имеется большой опыт в DataScience. Есть экспертиза в NLP и разработке моделей на pytorch. Пишите, обсудим детали:)
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