Comments (8)
I think you can. Haven't done this myself but I think you can summate/subtract the different embeddings of the different words in order to achieve what you are looking for.
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Thanks! I will give it a try somehow. Any chance of adding this as a feature to the predict function? Something like predict(model, text, exclude_docs=text, k=3)
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Maybe I'm misunderstanding the question. Predict already has argument basedoc which allows a similar thing to limit the prediction to that set of docs you provide in basedoc only (maybe not in the trainmode setting however that you refer to)
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I was aware of basedoc. I am just looking for a possibility to find similar documents while taking into account some dissimilar basedocs. I am sorry my question was not clear.
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why not just filter these out from basedoc by finding similarity from the text in basedoc and other docs which you do not want in
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I will figure some things out. Thanks!
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In addition to the question I asked above I want to give an example from word2vec tutorials. What I meant to say was, is it possible to do something like France + Berlin - Germany = Paris on a sentence or article level with Ruimtehol?
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Yes.
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Related Issues (20)
- find a way to solve Inf values in starspace_embedding due to numeric overflow
- Sentence separator for labelDoc format HOT 9
- Prediction of next word in a sentence HOT 6
- see if we can use proxyC https://cran.r-project.org/web/packages/proxyC/index.html for similarities instead of the current embedding_similarity implementation HOT 1
- unable to train wikipedia_shuf_train5M.txt HOT 2
- make function as_fasttext, using code from embed_tagspace
- allow option to use softmax to get probability-like results when using predict alongside model trained with embed_tagspace
- Running ruimtehol on R server HOT 18
- embed_tagspace produces different results within a session and when loaded (starspace_load_model) if ngrams is used HOT 11
- Problems with running ruimtehol on Windows R HOT 4
- non-virtual destructor
- example of manual calculation of embedding of doc
- Option of weighting words HOT 3
- Checkpointing: Continue model training at epoch x after saving intermediate model HOT 2
- Word embeddings HOT 6
- StarSpace Models in Shiny App HOT 8
- R CMD check FAIL on r-devel windows HOT 1
- switch to C++17 instead of C++11 as by cran policy HOT 1
- Request for sentiment scoring example HOT 1
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