Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling | general document classifier for labelled or unlabelled data | Data Labeling library

 by   Arghyadeep Python Version: Current License: No License

kandi X-RAY | Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling Summary

kandi X-RAY | Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling Summary

Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling is a Python library typically used in Artificial Intelligence, Data Labeling applications. Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling has no bugs, it has no vulnerabilities and it has low support. However Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling build file is not available. You can download it from GitHub.

A general document classifier for labelled or unlabelled data
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              Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling has a low active ecosystem.
              It has 4 star(s) with 2 fork(s). There are 2 watchers for this library.
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              It had no major release in the last 6 months.
              Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling is current.

            kandi-Quality Quality

              Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling has no bugs reported.

            kandi-Security Security

              Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling 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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              Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling releases are not available. You will need to build from source code and install.
              Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling and discovered the below as its top functions. This is intended to give you an instant insight into Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling implemented functionality, and help decide if they suit your requirements.
            • Create a pandas dataframe
            • Dump obj to a pickle file
            • Performs kfolds cross validation
            • Function to plot TSNE data
            • Plots the k - means in inertia scores
            • Compute tfidf from texts
            • Compute a dataframe from a list of texts
            • Run TSNE
            • Convert a list of texts into a dictionary
            • Print the top words
            • Untokenize a list of texts
            • Given a list of texts return a filtered list of texts
            • Read a text file
            • Return a subset of texts that match stop words
            • Convert tfidf to a list of vectors
            • Lemmatize a list of texts
            • Calculate the silhouette score for a given number of clusters
            • Convert texts into vectors
            • Plots the model accuracy
            • Get the best model selection
            • Select the best classifier based on the given hyperparameter
            • Dump obj to pickle_name
            Get all kandi verified functions for this library.

            Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling Key Features

            No Key Features are available at this moment for Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling.

            Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling Examples and Code Snippets

            No Code Snippets are available at this moment for Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling.

            Community Discussions

            QUESTION

            How can I do this split process in Python?
            Asked 2021-Dec-30 at 14:06

            I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

            What I've done so far is make this representation with the same enumerator class.

            A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

            ...

            ANSWER

            Answered 2021-Dec-30 at 13:57

            Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

            Source https://stackoverflow.com/questions/70532286

            QUESTION

            Replacing a character with a space and dividing the string into two words in R
            Asked 2020-Nov-18 at 07:32

            I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

            In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

            i have this:

            ...

            ANSWER

            Answered 2020-Nov-16 at 07:24

            QUESTION

            Azure ML FileDataset registers, but cannot be accessed for Data Labeling project
            Asked 2020-Oct-28 at 20:31

            Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

            Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

            Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

            ...

            ANSWER

            Answered 2020-Oct-27 at 22:39

            Is the data behind virtual network by any chance?

            Source https://stackoverflow.com/questions/64546521

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

            Vulnerabilities

            No vulnerabilities reported

            Install Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling

            You can download it from GitHub.
            You can use Document-Classification-with-Unsupervised-Learning-and-Topic-Modelling 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 .
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