multiannotator-benchmarks | Benchmarking algorithms for assessing quality | Data Labeling library

by cleanlab Jupyter Notebook Version: Current License: AGPL-3.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | multiannotator-benchmarks Summary

multiannotator-benchmarks is a Jupyter Notebook library typically used in Artificial Intelligence, Data Labeling applications. multiannotator-benchmarks has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Benchmarking algorithms for assessing quality of data labeled by multiple annotators

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Support

multiannotator-benchmarks has a low active ecosystem.

It has 13 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

multiannotator-benchmarks has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of multiannotator-benchmarks is current.

Quality

multiannotator-benchmarks has no bugs reported.

Security

multiannotator-benchmarks has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

multiannotator-benchmarks is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

multiannotator-benchmarks 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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multiannotator-benchmarks Key Features

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multiannotator-benchmarks Examples and Code Snippets

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Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

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ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

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ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

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

Vulnerabilities

No vulnerabilities reported

Install multiannotator-benchmarks

To run the model training and benchmark, you need to install the following dependencies:.
The cleanlab fork contains various multi-annotator algorithms studied in the benchmark (to obtain consensus labels and compute consensus and annotator quality scores) that are not present in the main library.
The crowd-kit fork addresses some numeric underflow issues in the original library (needed for properly ranking examples by their quality). Instead of operating directly on probabilities, our fork does calculations on log-probabilities with the log-sum-exp trick.

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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