fairseq-laser | My implementation of LASER architecture in Fairseq

 by   raymondhs Python Version: Current License: MIT

kandi X-RAY | fairseq-laser Summary

kandi X-RAY | fairseq-laser Summary

fairseq-laser is a Python library typically used in Internet of Things (IoT) applications. fairseq-laser has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However fairseq-laser build file is not available. You can download it from GitHub.

My implementation of LASER architecture in Fairseq
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            kandi-support Support

              fairseq-laser has a low active ecosystem.
              It has 10 star(s) with 4 fork(s). There are 2 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 7 have been closed. On average issues are closed in 7 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of fairseq-laser is current.

            kandi-Quality Quality

              fairseq-laser has no bugs reported.

            kandi-Security Security

              fairseq-laser has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              fairseq-laser 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

              fairseq-laser releases are not available. You will need to build from source code and install.
              fairseq-laser 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 fairseq-laser and discovered the below as its top functions. This is intended to give you an instant insight into fairseq-laser implemented functionality, and help decide if they suit your requirements.
            • Loads the model ensemble
            • Generate batches of input_fn
            • Reads files from the input buffer
            • Forward embedding
            • Map a dataset index to a dataset
            • Return the size of the given index
            • Perform the forward computation
            • Extract features from previous output
            • Compute the output layer
            Get all kandi verified functions for this library.

            fairseq-laser Key Features

            No Key Features are available at this moment for fairseq-laser.

            fairseq-laser Examples and Code Snippets

            No Code Snippets are available at this moment for fairseq-laser.

            Community Discussions

            Trending Discussions on Internet of Things (IoT)

            QUESTION

            Display data from two json files in react native
            Asked 2020-May-17 at 23:55

            I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files

            ...

            ANSWER

            Answered 2020-May-17 at 23:55

            The new object to get params in React Navigation 5 is:

            Source https://stackoverflow.com/questions/61859411

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

            Vulnerabilities

            No vulnerabilities reported

            Install fairseq-laser

            You can download it from GitHub.
            You can use fairseq-laser 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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          • HTTPS

            https://github.com/raymondhs/fairseq-laser.git

          • CLI

            gh repo clone raymondhs/fairseq-laser

          • sshUrl

            git@github.com:raymondhs/fairseq-laser.git

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