kandi X-RAY | FindSynonyms Summary
kandi X-RAY | FindSynonyms Summary
Ok, so here's how the .jar-File works. Just download the .jar, input.txt, and (for now) output.txt- File to the same dir. Assuming you have java installed, open your command window or terminal.
Top functions reviewed by kandi - BETA
- Main entry point
- Sends a request to the synonyms dictionary
- Find the synonyms for the given term
- Writes the synonyms to an OutputFile
FindSynonyms Key Features
FindSynonyms Examples and Code Snippets
Trending Discussions on FindSynonyms
As described, I load a trained word2vec model through pyspark....
ANSWERAnswered 2019-Nov-08 at 15:08
You can ensure that the object is dereferenced by the py4j gateway by running the following statement:
word2vec_model a pyspark
I am trying to read a text file in Spark-mllib examples (Word2VecExample) and create it word vectors. I run it by some text files and it gives no error but when reading one of my files, it gives this error and I am really confuse with that because I tried everything such as file format(utf-8) and ASCII characters. this is my source code:...
ANSWERAnswered 2019-Jun-01 at 11:58
Hello don't know much about Spark. Also can't post comments yet so an answer will have to do. Looking at the documention here.
.findSynonyms("string",num) "Find "num" number of words closest in similarity to the given word, not including the word itself."
So i can't help wondering is looking for the string "1" not maybe the problem. Off the top of my head i struggle to find 5 synonyms for "1", perhaps "one",maybe "uniary" or potentially "individual". From what I read Spark is a machine learning library so have you tried dumbing down the question? Maybe ask for a single synonym or give a simpler string to search for like "happy". This is just my two cents though, and mostly just curious about what is actually happening.
I'm trying to create a pre-trained embedding layer, using
h2o.word2vec, i'm looking to extract each word in the model and its equivalent embedded vector.
ANSWERAnswered 2018-Feb-06 at 16:22
you can use the method
(complete options are:
w2v_model.transform(words =, aggregate_method =)
words is an H2O Frame made of a single column containing source words (Note that you can specify to include a subset of this frame) and
aggregate_method specifies how to aggregate sequences of words.
if you don't specify an aggregation method, then no aggregation is performed, and each input word is mapped to a single word-vector. If the method is AVERAGE, then the input is treated as sequences of words delimited by NA.
My understanding is that word2vec can be ran in two modes:
- continuous bag-of-words (CBOW) (order of words does not matter)
- continuous skip-gram (order of words matters)
I would like to run the CBOW implementation from Spark's MLlib, but it is not clear to me from the documentation and their example how to do it. This is the example listed on their page.
ANSWERAnswered 2017-Sep-28 at 15:35
Seems like MLlib's currently only implements skip-gram.
Here is the open ticket/pull request for the skip-gram model: https://issues.apache.org/jira/browse/SPARK-20372
I am trying to load google's Pre-trained vectors 'GoogleNews-vectors-negative300.bin.gz' Google-word2vec into spark.
I converted the bin file to txt and created a smaller chunk for testing that I called 'vectors.txt'. I tried loading it as the following:...
ANSWERAnswered 2017-Aug-03 at 19:33
How exactly did you get vector.txt? If you read JavaDoc for Word2VecModel.save you may see that:
This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/
The model may be loaded using Loader.load.
So what you need is model in Parquet format which is standard for Spark ML models.
Unfortunately load from Google's native format has not been implemented yet (see SPARK-9484).
No vulnerabilities reported
You can use FindSynonyms like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the FindSynonyms component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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