FewShotPseudoLabeling | Few-shot Pseudo-Labeling for Intent Detection | Data Labeling library

 by   tdopierre Python Version: Current License: MIT

kandi X-RAY | FewShotPseudoLabeling Summary

kandi X-RAY | FewShotPseudoLabeling Summary

FewShotPseudoLabeling is a Python library typically used in Artificial Intelligence, Data Labeling, Pytorch applications. FewShotPseudoLabeling has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Few-shot Pseudo-Labeling for Intent Detection
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            kandi-support Support

              FewShotPseudoLabeling has a low active ecosystem.
              It has 5 star(s) with 0 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              FewShotPseudoLabeling has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of FewShotPseudoLabeling is current.

            kandi-Quality Quality

              FewShotPseudoLabeling has no bugs reported.

            kandi-Security Security

              FewShotPseudoLabeling has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              FewShotPseudoLabeling 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

              FewShotPseudoLabeling releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed FewShotPseudoLabeling and discovered the below as its top functions. This is intended to give you an instant insight into FewShotPseudoLabeling implemented functionality, and help decide if they suit your requirements.
            • Find the pseudo labels for each labeled dataset
            • Load data from a JSON file
            • Yield successive n - sized chunks from l
            • Return the list of unique labels in a tree
            • Runs a trained model
            • Run a test step
            • Compute loss
            • Euclidean distance
            • Find the pseudo labels for unlabeled data
            • Loads the model
            • Embed sentences
            • Tokenize a sentence
            • Finds the pseudo labels for the labeled data
            • Calculate the pseudopotential labels for the nkNN
            • Find the pseudo labels in the labeled file
            • Find the pseudo labels for the unlabeled dataset
            • Find the pseudo labels for the labeled dataset
            • Get command line arguments
            • Returns a dataset
            • Check the arguments
            • Save a list of dictionaries to a JSON file
            Get all kandi verified functions for this library.

            FewShotPseudoLabeling Key Features

            No Key Features are available at this moment for FewShotPseudoLabeling.

            FewShotPseudoLabeling Examples and Code Snippets

            No Code Snippets are available at this moment for FewShotPseudoLabeling.

            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 FewShotPseudoLabeling

            This repository uses the virtualenv environment.

            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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