auto-labeling-pipeline | doccano auto labeling pipeline helps doccano | Data Labeling library

 by   doccano Python Version: 0.1.23 License: MIT

kandi X-RAY | auto-labeling-pipeline Summary

kandi X-RAY | auto-labeling-pipeline Summary

auto-labeling-pipeline is a Python library typically used in Artificial Intelligence, Data Labeling, Deep Learning applications. auto-labeling-pipeline has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install auto-labeling-pipeline' or download it from GitHub, PyPI.

Auto labeling pipeline helps doccano to annotate a document automatically. This package is intended to be used from the inside of doccano. You shouldn't use this package directly.
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            kandi-support Support

              auto-labeling-pipeline has a low active ecosystem.
              It has 15 star(s) with 4 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 3 have been closed. On average issues are closed in 18 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of auto-labeling-pipeline is 0.1.23

            kandi-Quality Quality

              auto-labeling-pipeline has 0 bugs and 2 code smells.

            kandi-Security Security

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

            kandi-License License

              auto-labeling-pipeline 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

              auto-labeling-pipeline releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 894 lines of code, 94 functions and 20 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed auto-labeling-pipeline and discovered the below as its top functions. This is intended to give you an instant insight into auto-labeling-pipeline implemented functionality, and help decide if they suit your requirements.
            • Send a request to the API
            • Find and replace a value in a dictionary
            • Register a new option
            Get all kandi verified functions for this library.

            auto-labeling-pipeline Key Features

            No Key Features are available at this moment for auto-labeling-pipeline.

            auto-labeling-pipeline Examples and Code Snippets

            Auto labeling pipeline,Installation
            Pythondot img1Lines of Code : 1dot img1License : Permissive (MIT)
            copy iconCopy
            pip install auto-labeling-pipeline
              

            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 auto-labeling-pipeline

            To install auto-labeling-pipeline, simply run:.

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            Install
          • PyPI

            pip install auto-labeling-pipeline

          • CLONE
          • HTTPS

            https://github.com/doccano/auto-labeling-pipeline.git

          • CLI

            gh repo clone doccano/auto-labeling-pipeline

          • sshUrl

            git@github.com:doccano/auto-labeling-pipeline.git

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