text-classification-python | An example of retails products classification using scikit | Machine Learning library

by joaorafaelm Python Version: Current License: No License

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kandi X-RAY | text-classification-python Summary

text-classification-python is a Python library typically used in Artificial Intelligence, Machine Learning applications. text-classification-python has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

An example of retails products classification using scikit and nltk -

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text-classification-python has a low active ecosystem.

It has 21 star(s) with 12 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 783 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of text-classification-python is current.

Quality

text-classification-python has 0 bugs and 0 code smells.

Security

text-classification-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

text-classification-python code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

text-classification-python does not have a standard license declared.

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Without a license, all rights are reserved, and you cannot use the library in your applications.

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text-classification-python 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, examples and code snippets are available.

text-classification-python saves you 138 person hours of effort in developing the same functionality from scratch.

It has 346 lines of code, 6 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Fetch the products
Strip whitespace from a list
Return a field of a field
Pretty print a dictionary
Return a list of strings

Get all kandi verified functions for this library.

text-classification-python Key Features

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text-classification-python Examples and Code Snippets

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Community Discussions

Trending Discussions on text-classification-python

How to import(restore) Neural network model built by tflearn from files

Tensorflow: bag of words text classification: Cannot feed value of shape

QUESTION

How to import(restore) Neural network model built by tflearn from files

Asked 2018-Jun-12 at 14:49

I am referring to this tutorial on text classification and built a custom training set for a text classification.

I am saving the model with below code.

...

ANSWER

Answered 2018-Jun-12 at 14:49

I was able to restore the saved model with below code.

tflearn can restore model from saved log and model files.

Create dummy neural net of same size as saved model

Note : You may need to keep track of previously saved model's weights (size of input training and corresponding classes)

Source https://stackoverflow.com/questions/50220191

QUESTION

Tensorflow: bag of words text classification: Cannot feed value of shape

Asked 2018-Jan-11 at 10:44

I've nicked this code here: https://sourcedexter.com/tensorflow-text-classification-python/ to try to predict if a given question is either one of two categories.

However, I'm getting the following error:

Cannot feed value of shape (1, 1666) for Tensor 'TargetsData/Y:0', which has shape '(?, 2)'

Relevant code below:

...

ANSWER

Answered 2018-Jan-11 at 10:44

This is common shape mismatch error
Error is self-explanatory
Your target tensor is of shape [None, 2]
You are feeding you target tensor an array of (1, 1666)
Your model.fit() should be:

model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)
See the second parameter which is the target train_y but you have given the second parameter as train_x
which means you are saying that your inputs are you labels which is not true
May be that's why its throwing you the error

Source https://stackoverflow.com/questions/48204445

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

Vulnerabilities

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