Cross-Lingual Alignment of Contextual Word Embeddings
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Updated
Feb 12, 2020 - Python
Cross-Lingual Alignment of Contextual Word Embeddings
Topic Inference with Zeroshot models
Zero Shot Image Classification but more, Supports Multilingual labelling and a variety of CNN based models for a vision backbone by using OpenAI CLIP for $ conscious uses (Super simple, so a 10th-grader could totally write this but since no 10th-grader did, I did) - Prithivi Da
Alternate Implementation for Zero Shot Text Classification: Instead of reframing NLI/XNLI, this reframes the text backbone of CLIP models to do ZSC. Hence, can be lightweight + supports more languages without trading-off accuracy. (Super simple, a 10th-grader could totally write this but since no 10th-grader did, I did) - Prithivi Da
The source code of our ACM MM 2019 paper "TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning".
AdaptKeyBERT: keyword/keyphrase extraction with zero-shot and few-shot semi-supervised domain adaptation
Code for Generalized Zero-Shot Text Classification for ICD Coding (IJCAI 2020)
Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank, Malkin et al., NAACL 2022
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders
Advanced Feature Generating Networks for Zero-Shot Learning with Axial Attention transformer
零样本论文学习总结
Kickstart with LLMs
Glial-inspired ANN algorithm for unsupervised classification and zero-shot forecasting of nonstationary dynamics, based on dynamical systems and simplicial homotopy theory. Preprint of the manuscript available in the URL.
zeroshot-engine: Zero-Shot Text Classification with LLMs in Python
Zero Shot Classification with HuggingFace Pipeline
Ollama structured output for visual zeroshot reasoning
Slightly Tweaked implementation of the Zero-Shot Person Re-identification via Cross-View Consistency in Pytorch
Este repositório é um tutorial completo e prático que explora metodologias avançadas para a classificação de sentimentos e emoções em textos, utilizando Modelos de Linguagem de Grande Porte (LLMs). O projeto foi desenvolvido para ser acessível, com todos os exemplos executáveis em ambientes gratuitos como o Google Colab.
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