CollaboNet | CollaboNet for Biomedical Named Entity Recognition | Natural Language Processing library

by wonjininfo Python Version: Current License: Non-SPDX

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

kandi X-RAY | CollaboNet Summary

CollaboNet is a Python library typically used in Healthcare, Pharma, Life Sciences, Artificial Intelligence, Natural Language Processing, Deep Learning, Neural Network applications. CollaboNet has no bugs, it has no vulnerabilities and it has low support. However CollaboNet build file is not available and it has a Non-SPDX License. You can download it from GitHub.

This project provides a neural network(bi-LSTM + CRF) approach for biomedical Named Entity Recognition. Our implementation is based on the Tensorflow library on python.

Support

Quality

Security

License

Reuse

Support

CollaboNet has a low active ecosystem.

It has 40 star(s) with 9 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 7 have been closed. On average issues are closed in 56 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CollaboNet is current.

Quality

CollaboNet has 0 bugs and 0 code smells.

Security

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

CollaboNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CollaboNet has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

CollaboNet releases are not available. You will need to build from source code and install.

CollaboNet 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.

Top functions reviewed by kandi - BETA

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

Run the model
Generate summaries for a variable
Concatenate input_fc
Build a highway layer
Construct input wordVec
This function calculates the index of the word vocab
Load a wordvec file
Return True if string is a number
Perform dev1 epoch
This function calculates the padding of each sentence
Converts a batch group index to data
Pad the data in the batch
Embedding function
Embedding lookup
Run clwe embedding
Embedding char embedding
Train one epoch
Creates an IDT
Info1 epoch
Create a sentence from a text file
Set exp name

Get all kandi verified functions for this library.

CollaboNet Key Features

No Key Features are available at this moment for CollaboNet.

CollaboNet Examples and Code Snippets

No Code Snippets are available at this moment for CollaboNet.

Community Discussions

Trending Discussions on CollaboNet

Issues using tensorflow-gpu 1.7.0

QUESTION

Issues using tensorflow-gpu 1.7.0

Asked 2021-Apr-04 at 18:33

I want to test a model proposed here for that I need tensorflow-gpu version 1.7.0, I installed the required version using pip command pip install tensorflow-gpu==1.7.0 everything runs, but when I run the training I get the following error

...

ANSWER

Answered 2021-Apr-04 at 18:33

It seems like the problem was caused by an empty file which in consequence resulted in an empty feed dictionary.

Source https://stackoverflow.com/questions/66892241

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

Vulnerabilities

No vulnerabilities reported

Install CollaboNet

You can download it from GitHub.
You can use CollaboNet 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.