skweak Labelled data remains a scarce resource in many practical NLP scenarios. This is especially the case when working with resource-poor languages (or text domains), or when using task-specific labels without pre-existing datasets. The only available option is often to collect and annotate texts by hand, which is expensive and time-consuming. skweak (pronounced /skwi:k/) is a Python-based software toolkit that provides a concrete solution to this problem using weak supervision. skweak is built around a very simple idea: Instead of […]

Multilingual-CLIP OpenAI CLIP text encoders for any language. OpenAI recently released the paper Learning Transferable Visual Models From Natural Language Supervision in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representations using a contrastive learning objective. CLIP consists of two separate models, a visual encoder and a text encoder. These were trained on a wooping 400 Million images and corresponding captions. OpenAI has since released […]

FedNLP FedNLP is a research-oriented benchmarking framework for advancing federated learning (FL) in natural language processing (NLP). It uses FedML repository as the git submodule. In other words, FedNLP only focuses on adavanced models and dataset, while FedML supports various federated optimizers (e.g., FedAvg) and platforms (Distributed Computing, IoT/Mobile, Standalone). The figure below is the overall structure of FedNLP. avatar Installation After git clone-ing this repository, please run the following command to install our dependencies. conda create -n fednlp python=3.7 […]

Label Studio for Transformers Transfer learning for NLP models by annotating your textual data without any additional coding. This package provides a ready-to-use container that links together: Quick Usage Install Label Studio and other dependencies pip install -r requirements.txt Create ML backend with BERT classifier label-studio-ml init my-ml-backend –script models/bert_classifier.py cp models/utils.py my-ml-backend/utils.py Create ML backend with BERT named entity recognizer label-studio-ml init my-ml-backend –script models/ner.py cp models/utils.py my-ml-backend/utils.py Start ML backend at http://localhost:9090 label-studio-ml start my-ml-backend Start Label Studio […]