There are 1 repository under intent-recognition topic.
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
Rhasspy voice assistant for offline home automation
[RA-L20] Long-Term Pedestrian Trajectory Prediction Using Mutable Intention Filter and Warp LSTM
This repository is a comprehensive project that leverages the XLM-Roberta model for intent detection. This repository is a valuable resource for developers looking to build and fine-tune intent detection models based on state-of-the-art techniques.
DropSuit - NLP & data manipulation library for JS & Node.js. Offers diverse functions for text analysis, language understanding & more. Open-source under Apache License 2.0.
Constructing a dialogue chat bot, which will be able to: answer programming-related questions (using StackOverflow dataset); chit-chat and simulate dialogue on all non programming-related questions.
Athena is an Personal Assistant android app build using Api.ai for Intent recognition and 3rd party apis for different services.
Practical from my Social Signal Processing class
Simple voice assistant made for use in Russian
Bachelor's Thesis
Bachelor thesis regarding in-domain intent recognition and out-of-domain detection.
Constructing a corpus of ancient Chinese pediatric medicine literature, using algorithms such as BERT, lattice LSTM, and Siamese for tasks such as named entity recognition, intent recognition, entity similarity calculation, and entity linking, to develop a TCM-QA.
Project for my Conversational Agents course at UNITN.
The qaio function is a JavaScript and Node.js function that is part of the DropSuit NLP library. It is designed for response search by processing input strings or constructor input, with options of return type. Open-source and available under the Apache License 2.0.
The capability of an AI model (here, ChatGPT in particular) was tested in the sphere of customer service by availing data from real customers who had recently had experiences dealing with the chatbots or any other language model of an established institute or company.
English Intention Annotation Data