word2vec-tutorial | 中文詞向量訓練教學 | Topic Modeling library

 by   zake7749 Python Version: Current License: MIT

kandi X-RAY | word2vec-tutorial Summary

kandi X-RAY | word2vec-tutorial Summary

word2vec-tutorial is a Python library typically used in Artificial Intelligence, Topic Modeling applications. word2vec-tutorial has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However word2vec-tutorial build file is not available. You can download it from GitHub.

中文詞向量訓練教學
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              word2vec-tutorial has a low active ecosystem.
              It has 434 star(s) with 161 fork(s). There are 19 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 2 have been closed. On average issues are closed in 4 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of word2vec-tutorial is current.

            kandi-Quality Quality

              word2vec-tutorial has 0 bugs and 0 code smells.

            kandi-Security Security

              word2vec-tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              word2vec-tutorial code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              word2vec-tutorial is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              word2vec-tutorial releases are not available. You will need to build from source code and install.
              word2vec-tutorial has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed word2vec-tutorial and discovered the below as its top functions. This is intended to give you an instant insight into word2vec-tutorial implemented functionality, and help decide if they suit your requirements.
            • Main function .
            Get all kandi verified functions for this library.

            word2vec-tutorial Key Features

            No Key Features are available at this moment for word2vec-tutorial.

            word2vec-tutorial Examples and Code Snippets

            No Code Snippets are available at this moment for word2vec-tutorial.

            Community Discussions

            QUESTION

            Gensim iterating file from folder charmap codec can't decode byte
            Asked 2019-Sep-25 at 11:30

            I am following this Word2Vec tutorial. I want to make a gensim model, and first thing I want to do is try this code :

            ...

            ANSWER

            Answered 2019-Sep-25 at 07:39

            "codec can't decode byte 0x8" while reading a while?

            This is a very common problem which has a very common solution. You did not mention the encoding while reading the file.

            Try something like this while opening the file:

            Source https://stackoverflow.com/questions/58084214

            QUESTION

            how to speed up gensim word2vec initialization with pre proccessed corpus?
            Asked 2019-Apr-16 at 21:05

            i am training multiple word2vec models on the same corpus. (i am doing this to study the variation in learned word vectors)

            i am using this tutorial as reference: https://rare-technologies.com/word2vec-tutorial/

            it is suggested that by default gensim.models.word2vec will iterate over the corpus at least twice. once for initialization and then again for training (iterating the number of epochs specified)

            since i am always using the same corpus, i want to save time by initializing only once, and providing the same initialization as input to all successive models.

            how can this be done?

            this is my current setting:

            ...

            ANSWER

            Answered 2019-Apr-16 at 21:05

            If you supply a corpus of sentences to the class-instantiation, as your code has done, you don't need to call train(). It will already have done that automatically, and your second train() is redundant. (I recommend doing all such operations with logging enabled at the INFO level, and review the lgos after each run to understand what is happening – things like two full start-to-finish trainings should stick out in the logs.)

            The case where you would call train() explicitly is if you want more control over the interim steps. You leave the sentences out of the class-instantiation, but then it is required for you to perform two explicit steps: both one call to build_vocab() (for initial vocabulary scan) and then one call to train() (for actual multi-epoch training).

            In that case, you can use gensim's native .save() to save the model after the vocabulary-discovery, to have a model that's ready for re-training and doesn't need to report that step.

            So, you could re-load that vocabulary-built model multiple times, to different variables, to train in different ways. For some of the model's meta-parameters – like window or even dm mode – you can even tamper directly with their values on a model after vocabulary-building to try different variants.

            However, if there are any changes to the corpus's words/word-frequencies, or to other parameters that affect the initialization that happens during build_vocab() (like vector size), then the initialization will be out of sync with the configuration you're trying, and you could get strange errors.

            In such a case, the best course is to repeat the build_vocab() step entirely. (You could also look into the source code to see the individual steps performed by build_vocab(), and just patch/repeat the initialization steps that are needed, but that requires strong familiarity with the code.)

            Source https://stackoverflow.com/questions/55705634

            QUESTION

            word co-occurrence matrix from gensim
            Asked 2018-Mar-31 at 14:54

            When building a python gensim word2vec model, is there a way to see a doc-to-word matrix?

            With input of sentences = [['first', 'sentence'], ['second', 'sentence']] I'd see something like*:

            ...

            ANSWER

            Answered 2018-Mar-31 at 14:54

            The doc-word to word-word transform turns out to be more complex (for me at least) than I'd originally supposed. np.dot() is a key to its solution, but I need to apply a mask first. I've created a more complex example for testing...

            Imagine a doc-word matrix

            Source https://stackoverflow.com/questions/49431270

            QUESTION

            Code for gensim Word2vec as an HTTP service 'KeyedVectors' Attribute error
            Asked 2017-May-23 at 23:21

            I am using the w2v_server_googlenews code from the word2vec HTTP server running at https://rare-technologies.com/word2vec-tutorial/#bonus_app. I changed the loaded file to a file of vectors trained with the original C version of word2vec. I load the file with

            ...

            ANSWER

            Answered 2017-May-23 at 23:21

            FYI that demo code was baed on gensim 0.12.3 (from 2015, as listed in its requirements.txt), and would need updating to work with the latest gensim.

            It might be sufficient to add a line to w2v_server.py at line 70 (just after the load_word2vec_format()), to force the creation of the needed syn0norm property (which in older gensims was auto-created on load), before deleting the raw syn0 values. Specifically:

            Source https://stackoverflow.com/questions/44143441

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

            Vulnerabilities

            No vulnerabilities reported

            Install word2vec-tutorial

            You can download it from GitHub.
            You can use word2vec-tutorial like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/zake7749/word2vec-tutorial.git

          • CLI

            gh repo clone zake7749/word2vec-tutorial

          • sshUrl

            git@github.com:zake7749/word2vec-tutorial.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Consider Popular Topic Modeling Libraries

            gensim

            by RaRe-Technologies

            Familia

            by baidu

            BERTopic

            by MaartenGr

            Top2Vec

            by ddangelov

            lda

            by lda-project

            Try Top Libraries by zake7749

            Chatbot

            by zake7749Python

            PTT-Chat-Generator

            by zake7749Python

            DeepToxic

            by zake7749Jupyter Notebook

            Gossiping-Chinese-Corpus

            by zake7749Jupyter Notebook

            CIKM-AnalytiCup-2018

            by zake7749Python