keras-text | Text Classification Library in Keras | Machine Learning library

by raghakot Python Version: 0.1 License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | keras-text Summary

keras-text is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. keras-text has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install keras-text' or download it from GitHub, PyPI.

keras-text is a one-stop text classification library implementing various state of the art models with a clean and extendable interface to implement custom architectures.

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Quality

Security

License

Reuse

Support

keras-text has a low active ecosystem.

It has 419 star(s) with 101 fork(s). There are 20 watchers for this library.

It had no major release in the last 12 months.

There are 13 open issues and 4 have been closed. On average issues are closed in 100 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-text is 0.1

Quality

keras-text has 0 bugs and 13 code smells.

Security

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

keras-text code analysis shows 0 unresolved vulnerabilities.

There are 7 security hotspots that need review.

License

keras-text is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

keras-text releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

keras-text saves you 578 person hours of effort in developing the same functionality from scratch.

It has 1349 lines of code, 114 functions and 20 files.

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

Top functions reviewed by kandi - BETA

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

Encodes a list of texts
Update the counts
Append value to lst
A token generator
Build the tokenizer
Updates the counts
Create token indices
Apply encoding options
Generates tokens from texts
Apply work token filtering
Parse spacy keyword arguments
Return embeddings index
Build an embeddings index
Pad a sequence of sequences
Pad a list of sequences
Calculate the attention function
Softmax operation
Decode a list of encoded texts
Generate Markdown API documentation
Split the training and validation
Build the model
Update train and test indices
Convert a list of texts to unicode
Load a file
Serialize to a file
Saves the model to a file

Get all kandi verified functions for this library.

keras-text Key Features

No Key Features are available at this moment for keras-text.

keras-text Examples and Code Snippets

No Code Snippets are available at this moment for keras-text.

Community Discussions

Trending Discussions on keras-text

How to save one hot encoder?

How can I test my natural language processing model with "real" cases?

No module named 'keras_text_summarization' when running keras-text-summarization

sparse matrix length is ambiguous

QUESTION

How to save one hot encoder?

Asked 2019-Dec-17 at 09:57

I am trying to save a one hot encoder from keras to use it again on different texts but keeping the same encoding.

Here is my code :

...

ANSWER

Answered 2019-Dec-17 at 06:52

Mentioning the Answer in this Section (although it is present in Comments Section), for the benefit of the Community.

To Save the Encoder, you can use the below code:

Source https://stackoverflow.com/questions/58185671

QUESTION

How can I test my natural language processing model with "real" cases?

Asked 2019-Nov-15 at 16:19

I am introducing myself to Natural Languaje Processing and artificial neural networks and I have followed this wonderful tutorial Once finished it, I would like to know if there is any way to test the model with phrases that I can invent, (That film entertained me a lot) for example. Because it is very good to know the percentage of success on the test set, but I want to know how to test it.

...

ANSWER

Answered 2019-Nov-15 at 15:19

try

Source https://stackoverflow.com/questions/58879358

QUESTION

No module named 'keras_text_summarization' when running keras-text-summarization

Asked 2019-Mar-30 at 00:21

I have cloned keras-text-summarization, then was running according to README.md

python seq2seq_train.py and I get:

...

ANSWER

Answered 2018-Jul-04 at 12:45

The folder keras_text_summarization was outside the demo package. So as it appears the documentation is incorrect. I needed the either to:

Install the keras_text_summarization by running setup.py on parent folder.
Move keras_text_summarization inside the demo folder.

I ran step 2 and it worked (moved keras_text_summarization inside the demo folder).

This means no external modules were missing and condo installation was perfect.

Source https://stackoverflow.com/questions/51157258

QUESTION

sparse matrix length is ambiguous

Asked 2019-Mar-12 at 10:28

I'm very new to machine learning so this question might sound stupid. i'm following a tutorial on Text Classification but I'm facing an error that I don't have any idea about how to solve.

This is the code I have (it is basically what it is found in the tutorial)

...

ANSWER

Answered 2019-Mar-11 at 15:47

The reason you're facing this difficulty is that your X_train and X_test are of type whereas your model expects it to be a numpy array.

Try casting them to dense and you're fine to go:

Source https://stackoverflow.com/questions/55104059

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

Vulnerabilities

No vulnerabilities reported

Install keras-text

A dataset encapsulates tokenizer, X, y and the test set. This allows you to focus your efforts on trying various architectures/hyperparameters without having to worry about inconsistent evaluation. A dataset can be saved and loaded from the disk. The update_test_indices method automatically stratifies multi-class or multi-label data correctly.
See tests/ folder for usage. When dataset represented as (docs, words) word based models can be created using TokenModelFactory.
Yoon Kim CNN
Stacked RNNs
Attention (with/without context) based RNN encoders.
Yoon Kim CNN
Stacked RNNs
Attention (with/without context) based RNN encoders.
Hierarchical attention networks (HANs) can be build by composing two attention based RNN models. This is useful when a document is very large.
For smaller document a reasonable way to encode sentences is to average words within it. This can be done by using token_encoder_model=AveragingEncoder()
Mix and match encoders as you see fit for your problem.
Install keras with theano or tensorflow backend. Note that this library requires Keras > 2.0. keras-text uses the excellent spacy library for tokenization. See instructions on how to download model for target language.
Install keras with theano or tensorflow backend. Note that this library requires Keras > 2.0
Install keras-text
Download target spacy model

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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