uzaymacar/attention-mechanisms
Implementations for a family of attention mechanisms, suitable for all kinds of natural language processing tasks and compatible with TensorFlow 2.0 and Keras.
This project provides custom attention mechanisms for natural language processing (NLP) tasks. It takes sequences of text as input and helps improve the performance of models for tasks like sentiment classification, text generation, and machine translation. This is primarily useful for machine learning engineers or researchers building advanced NLP systems.
373 stars. No commits in the last 6 months.
Use this if you are building a natural language processing model in TensorFlow/Keras and need to integrate various attention mechanisms to enhance its understanding and generation capabilities for sequential data.
Not ideal if you are looking for a ready-to-use application or a high-level API for general text analysis without deep model customization.
Stars
373
Forks
87
Language
Python
License
MIT
Category
Last pushed
Feb 06, 2024
Commits (30d)
0
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