textaugment | TextAugment : Text Augmentation Library | Natural Language Processing library

 by   dsfsi Python Version: 2.0.0 License: MIT

kandi X-RAY | textaugment Summary

kandi X-RAY | textaugment Summary

textaugment is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Tensorflow, Bert applications. textaugment 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 textaugment' or download it from GitHub, PyPI.

TextAugment is a Python 3 library for augmenting text for natural language processing applications. TextAugment stands on the giant shoulders of NLTK, Gensim, and TextBlob and plays nicely with them.
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            kandi-support Support

              textaugment has a low active ecosystem.
              It has 146 star(s) with 25 fork(s). There are 4 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 8 open issues and 10 have been closed. On average issues are closed in 27 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of textaugment is 2.0.0

            kandi-Quality Quality

              textaugment has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              textaugment 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

              textaugment 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.
              textaugment saves you 276 person hours of effort in developing the same functionality from scratch.
              It has 669 lines of code, 43 functions and 11 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed textaugment and discovered the below as its top functions. This is intended to give you an instant insight into textaugment implemented functionality, and help decide if they suit your requirements.
            • Perform the flow computation
            • Mixup data
            • Validate input parameters
            • Augment the word2vec model
            • Gaussian distribution
            • Replace synonym with n words
            • Replace words with their respective synonyms
            • Gaussian function
            • Validate keyword arguments
            • Return a list of synonyms for a word
            • Delete a random word from a sentence
            • Swap a sentence
            • Swap two words
            • Insert n words into the sentence
            • Adds a word to the list
            • Augment the given string with the given language
            • Find the version from a file
            • Read content of file
            Get all kandi verified functions for this library.

            textaugment Key Features

            No Key Features are available at this moment for textaugment.

            textaugment Examples and Code Snippets

            No Code Snippets are available at this moment for textaugment.

            Community Discussions

            Trending Discussions on textaugment

            QUESTION

            Textual Data Augmentation in Tensorflow
            Asked 2021-Apr-24 at 18:21

            I'm doing a sentiment analysis on the IMDB dataset in tensorflow and I'm trying to augment the training dataset by using the textaugment library which they said is 'plug and play' into tensorflow. So it should be rather simple, but I'm new to tf so I'm not sure how to go about doing that. Here is what I have and what I am trying, based on reading the tutorials on the site.

            I tried to do a map to augment the training data but I got an error. You can scroll down to the last code block to see the error.

            ...

            ANSWER

            Answered 2021-Apr-24 at 18:21

            I am also trying to do the same. The error occurs because the textaugment function t.random_swap() is supposed to work on Python string objects.

            In your code, the function is taking in a Tensor with dtype=string. As of now, tensor objects do not have the same methods as Python strings. Hence, the error code.

            Nb. tensorflow_text has some additional APIs to work with such tensors of string types. Albeit, it is limited at the moment to tokenization, checking upper or lower case etc. A long winded workaround is to use the py_function wrapper but this reduces performance. Cheers and hope this helps. I opted not to use textaugment in the end in my use case.

            Nbb. tf.strings APIs have a bit more functionalities, such as regex replace etc but it is not complicated enough for your use case of augmentation. Would be helpful to see what others come up with, or if there are future updates to either TF or textaugment.

            Source https://stackoverflow.com/questions/66267633

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

            Vulnerabilities

            No vulnerabilities reported

            Install textaugment

            Install from pip [Recommended].

            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 textaugment

          • CLONE
          • HTTPS

            https://github.com/dsfsi/textaugment.git

          • CLI

            gh repo clone dsfsi/textaugment

          • sshUrl

            git@github.com:dsfsi/textaugment.git

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