fastBPE | Neural Machine Translation of Rare Words | Natural Language Processing library

 by   glample C++ Version: 0.1.0 License: MIT

kandi X-RAY | fastBPE Summary

kandi X-RAY | fastBPE Summary

fastBPE is a C++ library typically used in Artificial Intelligence, Natural Language Processing applications. fastBPE has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

C++ implementation of Neural Machine Translation of Rare Words with Subword Units, with Python API.
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              fastBPE has a low active ecosystem.
              It has 605 star(s) with 94 fork(s). There are 20 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 30 open issues and 24 have been closed. On average issues are closed in 29 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of fastBPE is 0.1.0

            kandi-Quality Quality

              fastBPE has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              fastBPE 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

              fastBPE releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

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            fastBPE Key Features

            No Key Features are available at this moment for fastBPE.

            fastBPE Examples and Code Snippets

            No Code Snippets are available at this moment for fastBPE.

            Community Discussions

            QUESTION

            Error Running "config = RobertaConfig.from_pretrained( "/Absolute-path-to/BERTweet_base_transformers/config.json""
            Asked 2020-Oct-24 at 17:16

            I'm trying to run the code 'transformers' version of this code to use the new pre-trained BERTweet model and I'm getting an error.

            The following lines of code ran successfully in my Google Colab notebook:

            ...

            ANSWER

            Answered 2020-Jun-16 at 12:15

            First of all you have to download the proper package as described in the github readme:

            Source https://stackoverflow.com/questions/62405867

            QUESTION

            Error while installing fastBPE package for translation chatbot
            Asked 2020-Oct-13 at 06:47

            I am trying to make a transalation chatbot with the help of a github repository. Initial step is to install fastBPE package. When running "pip install fastBPE" command in terminal, I am getting following error:

            ...

            ANSWER

            Answered 2020-Oct-13 at 06:47

            As far as i know it is not really made for windows. You could try to install it manually by following the instructions from this post.https://github.com/pytorch/fairseq/issues/1224

            Source https://stackoverflow.com/questions/64318798

            QUESTION

            How to execute a "g++" command on a Python server in Heroku?
            Asked 2019-Jun-17 at 09:16

            I am trying to deploy a Python server to Heroku, and I need to execute a "g++" command on one of the libraries to install it on the server.

            I want to create a gunicorn and Flask server hosting facebook's XLM model from cross-lingual model pretaining : https://github.com/facebookresearch/XLM

            The model requires the "fastBPE" library (https://github.com/glample/fastBPE), which requires to be installed with the command : g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast

            However, since the Heroku server is configured for Python, it doesn't recognize the "g++" command.

            Here is what I tried so far : - adding the buildpack “heroku-buildpack-apt” in Heroku and creating an "Aptfile" in my source file, to write "g++" inside of it, as well as "build-essential" - inside the main python file, I create a subprocess to launch "apt-get install g++" :

            ...

            ANSWER

            Answered 2019-Jun-17 at 09:16

            I managed to figure it out eventually.

            For posterity, there are 2 solutions that worked for me :

            1 - The not-so-good-one was to execute the g++ command on a Linux computer with exactly the same environment as the Heroku server, push it to Heroku and make sure to never modify it afterwards. You can then call fastBPE with a subprocess like above ==> it works, but it's more of a DIY unstable solution. The associated GitHub main file is https://github.com/Tony4469/xlm-agir/blob/master/mlm_tlm_prod.py

            2 - The best solution was to precompile everything on a Docker container with a Miniconda environment, you can install and run all the necessary commands and then push it easily to heroku. You can find the Dockerfile I used here : https://github.com/Tony4469/laser-agir/blob/master/Dockerfile

            Source https://stackoverflow.com/questions/56540601

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

            Vulnerabilities

            No vulnerabilities reported

            Install fastBPE

            You can download it from GitHub.

            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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            Install
          • PyPI

            pip install fastBPE

          • CLONE
          • HTTPS

            https://github.com/glample/fastBPE.git

          • CLI

            gh repo clone glample/fastBPE

          • sshUrl

            git@github.com:glample/fastBPE.git

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