CAMeLBERT | Task Type in Arabic Pre | Natural Language Processing library

by CAMeL-Lab Python Version: Current License: MIT

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kandi X-RAY | CAMeLBERT Summary

CAMeLBERT is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Bert applications. CAMeLBERT 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.

This repo contains code for the experiments presented in our paper: The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models.

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CAMeLBERT has a low active ecosystem.

It has 33 star(s) with 7 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 4 have been closed. On average issues are closed in 16 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CAMeLBERT is current.

Quality

CAMeLBERT has 0 bugs and 33 code smells.

Security

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

CAMeLBERT code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

CAMeLBERT is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

It has 1651 lines of code, 65 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Train model
Evaluate the model
Calculates the acc and F1D and F1D
Compute the metrics for the given task
Loads the training examples from the given directory
Create a list of InputExample objects
Process Tweet
Get dev examples
Create input examples from text
Load train examples from the given directory
Get dev examples from MADAR
Get test examples from arSAS
Gets test examples from the tsv file
Gets test examples from MADAR
Loads the test examples from the data directory
Parses the MADAR dataset
Returns a list of dev examples
Evaluate a model
Write the most common prediction to a file
Return a dict of user prediction predictions
Get dev examples from tsv file
Gets training examples from the training data directory
Reads the labels from file
Get train examples from the given directory
Get dev examples from a directory
Get test examples from MADAR

Get all kandi verified functions for this library.

CAMeLBERT Key Features

No Key Features are available at this moment for CAMeLBERT.

CAMeLBERT Examples and Code Snippets

No Code Snippets are available at this moment for CAMeLBERT.

Community Discussions

Trending Discussions on CAMeLBERT

Training CamelBERT model for token classification

QUESTION

Training CamelBERT model for token classification

Asked 2022-Mar-16 at 11:34

I am trying to use a huggingface model (CamelBERT) for token classification using ANERCorp Dataset. I fed the training set from ANERCorp to train the model, but I am getting the following error.

Error:

...

ANSWER

Answered 2022-Mar-16 at 11:34

The script you are using loads the labels from $DATA_DIR/train.txt.

See https://github.com/CAMeL-Lab/CAMeLBERT/blob/master/token-classification/run_token_classification.py#L105 for what the model expects.

It then tries to load the label list as first file file from the corpus (even before loading the training data), see https://github.com/CAMeL-Lab/CAMeLBERT/blob/master/token-classification/run_token_classification.py#L183 and put it into label_map.

But that fails for some reason. My assumption would be that it doensnt find anything and label_map is an empty dict, so the first attempt to get the labels from it fails with KeyError. Probably either your input data is not there or not in the path as expected (check if you have the right files and the right value for $DATA_DIR). From my experience relative paths in Google Drive can be tricky. Try something simple to see if it works, like os.listdir($DATA_DIR) to see if that is actually the directly you expect it to be.

If that is not the problem then probably something about the labels is actually wrong. Does ANERCorp use this exact way of writing labels (B-LOC etc.)? If it is different (e.g. B-Location or something) it would fail too.

Source https://stackoverflow.com/questions/71493915

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

Vulnerabilities

No vulnerabilities reported

Install CAMeLBERT

You can download it from GitHub.
You can use CAMeLBERT 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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