Topic-Seg-Label | Weakly Supervised Topic Segmentation and Labeling | Data Labeling library

by truthless11 Python Version: Current License: No License

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

kandi X-RAY | Topic-Seg-Label Summary

Topic-Seg-Label is a Python library typically used in Artificial Intelligence, Data Labeling applications. Topic-Seg-Label has no bugs, it has no vulnerabilities and it has low support. However Topic-Seg-Label build file is not available. You can download it from GitHub.

Weakly Supervised Topic Segmentation and Labeling

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Quality

Security

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Support

Topic-Seg-Label has a low active ecosystem.

It has 25 star(s) with 6 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 0 have been closed. On average issues are closed in 90 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Topic-Seg-Label is current.

Quality

Topic-Seg-Label has 0 bugs and 0 code smells.

Security

Topic-Seg-Label has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Topic-Seg-Label code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Topic-Seg-Label 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

Topic-Seg-Label releases are not available. You will need to build from source code and install.

Topic-Seg-Label has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Topic-Seg-Label saves you 229 person hours of effort in developing the same functionality from scratch.

It has 560 lines of code, 30 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 Topic-Seg-Label and discovered the below as its top functions. This is intended to give you an instant insight into Topic-Seg-Label implemented functionality, and help decide if they suit your requirements.

Train the model
Generate a reward for each observation
Compute the similarity of the given batch of observations
Choose an action from the model
Train a model
Generate batch data
Run training step
Pre - learn model
Pre learn function
Calculate the reward score
Explicitly exploit a model
Verify loss function
Evaluate the objective function
Evaluate the model
Evaluate an SNN model
Runs the training step
Generate predicted labels
Applies an attack
Load dataset
Generate hidden states
Build word vectors from file

Get all kandi verified functions for this library.

Topic-Seg-Label Key Features

No Key Features are available at this moment for Topic-Seg-Label.

Topic-Seg-Label Examples and Code Snippets

No Code Snippets are available at this moment for Topic-Seg-Label.

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:

...

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.

...

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 Topic-Seg-Label

You can download it from GitHub.
You can use Topic-Seg-Label 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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