arabert | trained Transformers for the Arabic Language Understanding | Natural Language Processing library

by aub-mind Python Version: 1.0.1 License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | arabert Summary

arabert is a Python library typically used in Artificial Intelligence, Natural Language Processing, Bert applications. arabert has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can install using 'pip install arabert' or download it from GitHub, PyPI.

This repository now contains code and implementation for:.

Support

Quality

Security

License

Reuse

Support

arabert has a low active ecosystem.

It has 459 star(s) with 119 fork(s). There are 25 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 52 have been closed. On average issues are closed in 11 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of arabert is 1.0.1

Quality

arabert has 0 bugs and 0 code smells.

Security

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

arabert code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

arabert does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

arabert 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 are not available. Examples and code snippets are available.

arabert saves you 5555 person hours of effort in developing the same functionality from scratch.

It has 12815 lines of code, 641 functions and 66 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed arabert and discovered the below as its top functions. This is intended to give you an instant insight into arabert implemented functionality, and help decide if they suit your requirements.

Convert examples to features
Return a string representation of text
Convert a single Example
Truncate a sequence pair
Writes predictions to a file
Get the final prediction
Compute softmax
Return the n_best_size n_best_size
Sample a token sequence
Postprocessing postprocessing
Run Finetuning
Create TrainingInstances
Perform GPT2 attention
Embed word embedding
Validates that the case is correct
Tokenize text
Write examples
Builds an input function
Build a model function for TPUEstimator
Perform featurization
Embedding postprocessor
Mask input tensors
Writes examples to examples
Feature features
Reads squad examples
Transformer transformer model

Get all kandi verified functions for this library.

arabert Key Features

No Key Features are available at this moment for arabert.

arabert Examples and Code Snippets

KeyError: 'true ' error when i try to convert labels to 0 and 1

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

Data[LABEL_COLUMN] = Data[LABEL_COLUMN].apply(lambda x: label_map[x.strip()])

Community Discussions

Trending Discussions on arabert

KeyError: 'true ' error when i try to convert labels to 0 and 1

QUESTION

KeyError: 'true ' error when i try to convert labels to 0 and 1

Asked 2020-Oct-31 at 10:07

I am using Arabert (pre trained Bert for Arabic language) for binary classification labeled as true and false, i am trying to change the labels from "true" and "false" to 0 and one i used the code:

...

ANSWER

Answered 2020-Oct-31 at 10:07

These is a trailing space in 'true ', that's why there is no match in label_map, try:

Source https://stackoverflow.com/questions/64620423

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

Vulnerabilities

No vulnerabilities reported

Install arabert

You can install using 'pip install arabert' or download it from GitHub, PyPI.
You can use arabert 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.