zold.github.io | Zold website and JavaScript assets

 by   zold-io JavaScript Version: 0.15.1 License: No License

kandi X-RAY | zold.github.io Summary

kandi X-RAY | zold.github.io Summary

zold.github.io is a JavaScript library. zold.github.io has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Here is the White Paper. Join our Telegram group to discuss it all live. The website is here: www.zold.io.
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              zold.github.io has a low active ecosystem.
              It has 16 star(s) with 4 fork(s). There are 6 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 9 open issues and 98 have been closed. On average issues are closed in 25 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of zold.github.io is 0.15.1

            kandi-Quality Quality

              zold.github.io has 0 bugs and 0 code smells.

            kandi-Security Security

              zold.github.io has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              zold.github.io code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              zold.github.io does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              zold.github.io releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.
              zold.github.io saves you 347 person hours of effort in developing the same functionality from scratch.
              It has 831 lines of code, 0 functions and 21 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            zold.github.io Key Features

            No Key Features are available at this moment for zold.github.io.

            zold.github.io Examples and Code Snippets

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            Community Discussions

            Trending Discussions on zold.github.io

            QUESTION

            Extracting Word Frequency List from a Large Corpus
            Asked 2019-Jan-13 at 17:56

            I have a large English corpus named SubIMDB and I want to make a list of all the words with their frequency. Meaning that how much they have appeared in the whole corpus. This frequency list should have some characteristics:

            1. The words like boy and boys or other grammatical features such as get and getting, the same word or lemma and if there are 3 boy and 2 boys it should list them as Boy 5. However, not for the cases like Go and Went which have irregular forms(or foot and feet)
            2. I want to use this frequency list as a kind of dictionary so whenever I see a word in another part of the program I want to check its frequency in this list. So, better if it is searchable without looking up all the of it.

            My questions are:

            1. For the first problem, what should I do? Lemmatize? or Stemming? or how can I get that?
            2. For second, what kind of variable type I should set it to? like dictionary or lists or what?
            3. Is is the best to save it in csv?
            4. Is there any prepared toolkit for python doing this all?

            Thank you so much.

            ...

            ANSWER

            Answered 2019-Jan-13 at 17:56

            As pointed above, question(s) is a opinion based and vague, but here's some directions:

            1. Both will work for your case. Stemming usually is simpler and faster. I suggest starting with nltk's PorterStemmer. If you need sophisticated lemmatization, take a look at spaCy, IMO that's industry standard.
            2. You need dictionary, which gives you amortized O(1) lookup once you have your stem/lemma. Also counter may become useful.
            3. Depends on your usecase. CSV is more "portable", pickle may be easier to use.
            4. There's a lot of "building blocks" in nltk and spaCy, building your pipeline/models is up to you

            Source https://stackoverflow.com/questions/54171178

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install zold.github.io

            You can download it from GitHub.

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            First, install Node.js+NPM and Grunt. Then:. The build has to be clean. If it's not, submit an issue. Then, make your changes, make sure the build is still clean, and submit a pull request.
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