text-classification-keras | 📚 Text classification library with Keras | Natural Language Processing library

by jfilter Python Version: 0.1.4 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | text-classification-keras Summary

text-classification-keras is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Tensorflow, Keras, Neural Network applications. text-classification-keras 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 text-classification-keras' or download it from GitHub, PyPI.

A high-level text classification library implementing various well-established models. With a clean and extendable interface to implement custom architectures.

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text-classification-keras has a low active ecosystem.

It has 54 star(s) with 11 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 11 have been closed. On average issues are closed in 133 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of text-classification-keras is 0.1.4

Quality

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

Security

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

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

There are 0 security hotspots that need review.

License

text-classification-keras 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

text-classification-keras releases are not available. You will need to build from source code and install.

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.

text-classification-keras saves you 955 person hours of effort in developing the same functionality from scratch.

It has 2175 lines of code, 172 functions and 41 files.

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

Top functions reviewed by kandi - BETA

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

Generates tokens from texts
Apply work token filtering
Parse spacy keyword arguments
Train a Keras model
Builds the model
Build embeddings for each word in the corpus
Copies the last call to exp_path
Calculates the train and test indices
Computes the indices of the equally spaced distributions over each class
Calculate the attention function
Softmax function
Sets up training and validation
Process a save
Add n - grams to the corpus
Save the configuration to a file
Setup tokenizer
Generate the Markdown API docs
Convert a module to markdown
Convert a function to markdown
Convert a class to markdown
Load train and test dataset
Load a file
Setup embeddings
Build the RNN model
Returns the attention tensor

Get all kandi verified functions for this library.

text-classification-keras Key Features

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

text-classification-keras Examples and Code Snippets

Text Classification Keras ,Quick start,Usage

Python

Lines of Code : 34

License : Permissive (MIT)

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from texcla import experiment, data
from texcla.models import TokenModelFactory, YoonKimCNN
from texcla.preprocessing import FastTextWikiTokenizer

# input text
X = ['some random text', 'another random text lala', 'peter', ...]

# input labels
y = ['

Text Classification Keras ,Advanced,Models

Python

Lines of Code : 16

License : Permissive (MIT)

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from texcla.models import TokenModelFactory, YoonKimCNN

factory = TokenModelFactory(tokenizer.num_classes, tokenizer.token_index,
    max_tokens=100, embedding_type='glove.6B.100d')
word_encoder_model = YoonKimCNN()
model = factory.build_model(token

Text Classification Keras ,Citation

Python

Lines of Code : 7

License : Permissive (MIT)

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@misc{raghakotfiltertexclakeras
    title={Text Classification Keras},
    author={Raghavendra Kotikalapudi, and Johannes Filter, and contributors},
    year={2018},
    publisher={GitHub},
    howpublished={\url{https://github.com/jfilter/text-class

Community Discussions

Trending Discussions on text-classification-keras

Setting up a functional model in Keras

QUESTION

Setting up a functional model in Keras

Asked 2018-May-28 at 17:06

I'm just messing around with Keras for fun (continuing education) and am having some issues with specifying the data structure in a CNN.

...

ANSWER

Answered 2018-May-28 at 17:06

You have sparse_categorical_crossentropy which expects just the integer labels of classes whereas you give encoded versions already (18,). As such, you need to change loss='categorical_crossentropy' to fix the problem.

Source https://stackoverflow.com/questions/50570808

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

Vulnerabilities

No vulnerabilities reported

Install text-classification-keras

The [full] will additionally install TensorFlow, Spacy, and Deep Plots. Choose this if you want to get started right away.

Support

If you have a question, found a bug or want to propose a new feature, have a look at the issues page. Pull requests are especially welcomed when they fix bugs or improve the code quality.

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