sequence_tagging | using bilstm-crf , bert and other methods to do sequence | Natural Language Processing library

by qiufengyuyi Python Version: Current License: No License

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

kandi X-RAY | sequence_tagging Summary

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

using bilstm-crf,bert and other methods to do sequence tagging task

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Quality

Security

License

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Support

sequence_tagging has a low active ecosystem.

It has 293 star(s) with 57 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 3 have been closed. On average issues are closed in 109 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sequence_tagging is current.

Quality

sequence_tagging has 0 bugs and 0 code smells.

Security

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

sequence_tagging code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sequence_tagging 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

sequence_tagging 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.

sequence_tagging saves you 3182 person hours of effort in developing the same functionality from scratch.

It has 6844 lines of code, 392 functions and 52 files.

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

Top functions reviewed by kandi - BETA

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

Build a tf TPUEstimator
Determine whether a weight decay is used
Create an optimizer
Applies gradients
Creates a model
Transformer model
Attention layer
Get the shape of a tensor
Apply dropout
Compute the attention layer
Create a training op
Tokenize text
Embed word embedding
Recognize confusion matrix
Builds a tf input function
Create TrainingInstances
Run bertWtorch
Compute precision precision for confusion matrix classification
Create a BertModel
Builds the input function
Run train
Embedding postprocessor
Convert examples to features
Run bertM
Writes examples to examples
Run BERT
Load a word character from the original data

Get all kandi verified functions for this library.

sequence_tagging Key Features

No Key Features are available at this moment for sequence_tagging.

sequence_tagging Examples and Code Snippets

No Code Snippets are available at this moment for sequence_tagging.

Community Discussions

Trending Discussions on sequence_tagging

Why doesn't Tensorflow automatically handle hidden states of recurrent cells?

QUESTION

Why doesn't Tensorflow automatically handle hidden states of recurrent cells?

Asked 2018-Feb-26 at 19:27

I am going through couple of Tensorflow examples that use LSTM cells and trying to understand the purpose of initial_state variable that is used in one implementation but not in the other for some unknown reason.

For example PTB example uses it as:

...

ANSWER

Answered 2018-Feb-26 at 19:27

As I understood, it it appears to be specific setup for Tensorflow PTB model which is supposed to be running not only with single LSTM cells but with several ones (who would even try to train it on more than 2 cells I wonder). For that it needs to keep track of c and h tensors between the cells and thus the _initial_state variable. It also is supposed to be running in parallel over several GPUs as well, continue if interrupted etc. And that is why PTB example code looks ugly and overengineered to a newcomer.

Source https://stackoverflow.com/questions/48911479

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

Vulnerabilities

No vulnerabilities reported

Install sequence_tagging

You can download it from GitHub.
You can use sequence_tagging 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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