# Inputing word2vec#wordsNearest only positive words in prediction step **URL:** \ **Category:** DL4J\ **Created:** [September 30, 2020, 2:38pm UTC](https://community.konduit.ai/t/inputing-word2vec-wordsnearest-only-positive-words-in-prediction-step/896 "2020-09-30T14:38:19Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![bacloud14](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/bacloud14/32/327_2.png) [@bacloud14](https://community.konduit.ai/u/bacloud14)\ **Post date:** [September 30, 2020, 2:38pm UTC](https://community.konduit.ai/t/inputing-word2vec-wordsnearest-only-positive-words-in-prediction-step/896/1 "2020-09-30T14:38:19Z")
Hi there, I copy my same question from StackOverflow here, hoping for a more sure response: Kindly, forgive that I am a noob in word embedding in general. I have not only a word to predict similarity, but a list of words sometimes (sentence). All examples of `word2vec` in `deeplearning4j` train with a list of words but predict with a single word. I know word2vec is capable for doing complex logical inferences, like what’s most similar to a king and a woman that is not a queen. But for my case, I assume it is just additions, like what the most similar to “a big cat”, it would be a tiger. I tried: ``` INDArray wordVector1 = wordVectors.getWordVectorMatrix(sentence); Collection lst_2 = wordVectors.wordsNearest(wordVector1, 10); ``` Where sentence is a String. I also tried ``` Collection lst_2 = wordVectors.wordsNearest(sentence, 10); ``` But it yields in the first `nullPointerException` and in the second an empty array, where I believe it shouldn’t. I see another signature of `wordVectors#wordsNearest` accepting an “INDArray” but I don’t know what that is except that is a high performant data-structure. Also seeing example [here](https://balrog.ai.wu.ac.at/svn/projects/MinicBac/FirstTests.java) in “Tried using the wordNearest with only positive vectors” snippet, I tried: ``` Collection lst_2 = wordVectors.wordsNearest(Arrays.asList(sentence.split(" ")), 10); ``` And **org.deeplearning4j version = 0.9.1** is complaining about the type. Like one input is a collection, another collection is needed. Any hints ? ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [September 30, 2020, 10:25pm UTC](https://community.konduit.ai/t/inputing-word2vec-wordsnearest-only-positive-words-in-prediction-step/896/2 "2020-09-30T22:25:55Z")
@bacloud14 > “INDArray” please always make sure to understand the fundamentals. Stepping in to machine learning without knowing the basics of ndarrays is always going to be fraught with rabbit holes. > Dl4j 0.9.x > Dl4j 0.9.x is over 2 years old and shouldn’t be used. Our quickstart covers the main way you should get started: > [https://deeplearning4j.konduit.ai/getting-started/quickstart](https://deeplearning4j.konduit.ai/getting-started/quickstart) We have examples that do this out of the box, our quickstart links directly to the latest examples: > > > //github.com/deeplearning4j/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/advanced/modelling/embeddingsfromcorpus/word2vec Word2vec isn’t as amazing as you think it is. Take a step back and look at NLP basics before just diving in trying to use a model for something it’s not meant for 🙂 I would look at even the bare most of NLP basics before you start diving in to these kinds of things. One key concept is an ngram: [n-gram - Wikipedia](https://en.wikipedia.org/wiki/N-gram) Another is a vocabulary. All models have a set of possible words they learned including word2vec: [https://deeplearning4j.konduit.ai/language-processing/word2vec](https://deeplearning4j.konduit.ai/language-processing/word2vec) You can’t just magic a word in to existence (let alone multiple words) The models in dl4j work on a single unigram or 1 word. You can _train_ word2vec on bi or trigrams if you want but that would mean building a new model. If you want to do that, I can show you how to proceed. ---
**Author:** ![bacloud14](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/bacloud14/32/327_2.png) [@bacloud14](https://community.konduit.ai/u/bacloud14)\ **Post date:** [October 1, 2020, 12:43pm UTC](https://community.konduit.ai/t/inputing-word2vec-wordsnearest-only-positive-words-in-prediction-step/896/3 "2020-10-01T12:43:38Z")
Thank you @agibsonccc for your valuable reply. I admit that I am new and that probably the way I presented the problem was like a bottle in the sea or incoherent. I am a generalist programmer with an interest in Data science lately, and in this specific case, I am looking for a precipitate solution for a tiny Android application I am making, again, I know these algorithms are tricky. Finally, digging examples and API, I could train on ngrams ``` String filePath = new ClassPathResource("myfile.shuffled").getFile().getAbsolutePath(); SentenceIterator iter = new BasicLineIterator(filePath); TokenizerFactory t = new NGramTokenizerFactory(new DefaultTokenizerFactory(), 1, 3); //new DefaultTokenizerFactory(); t.setTokenPreProcessor(new CommonPreprocessor()); ``` and ``` Word2Vec vec = new Word2Vec.Builder() .minWordFrequency(10) .layerSize(64) .windowSize(5) .iterate(iter) .tokenizerFactory(t) .build(); LOGGER.info("Fitting Word2Vec model...."); vec.fit(); ``` It is giving fair results on my real world prediction data. Thanks again. ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [October 2, 2020, 2:17pm UTC](https://community.konduit.ai/t/inputing-word2vec-wordsnearest-only-positive-words-in-prediction-step/896/4 "2020-10-02T14:17:03Z")
> [@bacloud14](#): > > ate solution for a tiny Android application I am making, again, I know these algorithms are tricky. @bacloud14 great to hear! Good luck then!