Cross-Lingual Alignment of Contextual Word Embeddings
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Updated
Feb 12, 2020 - Python
Cross-Lingual Alignment of Contextual Word Embeddings
Language Modelling Makes Sense - WSD (and more) with Contextual Embeddings
In the wild extraction of entities that are found using Flair and displayed using a very elegant front-end.
BERTMap: A BERT-Based Ontology Alignment System
Model zoo for topic models, neural topic models, contextual embeddings for topic models ...
Applied Deep Learning (2019 Spring) @ NTU
A simple wrapper class for extracting features(embedding) and comparing them using BERT in TensorFlow
Exploring semantic similarities between contextualized embeddings
Code for reproducing our paper: LMSOC: An Approach for Socially Sensitive Pretraining
[ICADL] Named entity recognition architecture combining contextual and global features
Debiasing both contextual and context free embeddings and developing a downstream metric for bias measurement
Code and data for the paper 'Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings'
Empirical Comparison of Dimensionality Reduction Techniques for Contextual Embeddings over Low Sample Human-Level Tasks
Semester project for Machine Learning: Deep Learning, Spring 2020
Vajrayana AI Chat Web App (Python/Django/HTMX/OpenAI API). Create and converse with your own AI experts on Vajrayana Buddhism based on teachings from prominent Tibetan Tulkus and teachers.
Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encoding knowledge that transfers across languages. in this repository we tend to implement this concept using Recurrent Neural Networks.
We aim to clasify tweets based on three categories 0: hate, 1: offensive, 2: neutral. For this purpose we use HateBert pretrained model with RNNs as the trainable layers
Contextual embeddings are able to encode word meaning and polysemy to some degree. However, richer semantic information requires using representations other than texts, like knowledge graphs (KG). The goal of this project is to design a model to combining contextual and KG embeddings.
It shows an analytic agent based on advanced RAG.
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