lstm-context-embeddings | Augmenting word embeddings with their surrounding | Machine Learning library
kandi X-RAY | lstm-context-embeddings Summary
kandi X-RAY | lstm-context-embeddings Summary
lstm-context-embeddings is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. lstm-context-embeddings has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However lstm-context-embeddings build file is not available. You can download it from GitHub.
Presented here is a method to modify the word embeddings of a word in a sentence with its surrounding context using a bidirectional Recurrent Neural Network (RNN). The hypothesis is that these modified embeddings are a better input for performing text classification tasks like sentiment analysis or polarity detection.
Presented here is a method to modify the word embeddings of a word in a sentence with its surrounding context using a bidirectional Recurrent Neural Network (RNN). The hypothesis is that these modified embeddings are a better input for performing text classification tasks like sentiment analysis or polarity detection.
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Quality
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lstm-context-embeddings has a low active ecosystem.
It has 57 star(s) with 21 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 5 have been closed. On average issues are closed in 281 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of lstm-context-embeddings is current.
Quality
lstm-context-embeddings has no bugs reported.
Security
lstm-context-embeddings has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
lstm-context-embeddings is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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lstm-context-embeddings releases are not available. You will need to build from source code and install.
lstm-context-embeddings 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 lstm-context-embeddings and discovered the below as its top functions. This is intended to give you an instant insight into lstm-context-embeddings implemented functionality, and help decide if they suit your requirements.
- Load data and labels
- Clean a string
- Iterate over data
Get all kandi verified functions for this library.
lstm-context-embeddings Key Features
No Key Features are available at this moment for lstm-context-embeddings.
lstm-context-embeddings Examples and Code Snippets
No Code Snippets are available at this moment for lstm-context-embeddings.
Community Discussions
Trending Discussions on lstm-context-embeddings
QUESTION
Tensorflow 1.4 Bidirectional RNN not working as expected
Asked 2018-Jan-16 at 14:50
I am trying to use Bidirectional RNN and pass the output through a CNN for text classification. However, I am getting all sorts of shape errors with bidirectional RNN. Although, If I use two dynamic rnn with reverse op in the second layer, it appears to work fine:
Here is bidirectional RNN code that DOES NOT work for me:
...ANSWER
Answered 2018-Jan-16 at 14:50All I had to do was multiply the hidden size by 2 since output size of birrectional RNN is twice that of rnn.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install lstm-context-embeddings
You can download it from GitHub.
You can use lstm-context-embeddings 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.
You can use lstm-context-embeddings 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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