InferSent InferSent is a sentence embeddings method that provides semantic representations for English sentences. It is trained on natural language inference data and generalizes well to many different tasks. We provide our pre-trained English sentence encoder from our paper and our SentEval evaluation toolkit. Recent changes: Removed train_nli.py and only kept pretrained models for simplicity. Reason is I do not have time anymore to maintain the repo beyond simple scripts to get sentence embeddings. Dependencies This code is written in […]

The multitask and transfer learning toolkit for natural language processing research. Why should I use jiant? A few additional things you might want to know about jiant: jiant is configuration file driven jiant is built with PyTorch jiant integrates with datasets to manage task data jiant integrates with transformers to manage models and tokenizers. Getting Started Installation To import jiant from source (recommended for researchers): git clone https://github.com/nyu-mll/jiant.git cd jiant pip install -r requirements.txt # Add the following to your […]

MUSE: Multilingual Unsupervised and Supervised Embeddings A library for Multilingual Unsupervised or Supervised word Embeddings. MUSE is a Python library for multilingual word embeddings, whose goal is to provide the community with: state-of-the-art multilingual word embeddings (fastText embeddings aligned in a common space) large-scale high-quality bilingual dictionaries for training and evaluation We include two methods, one supervised that uses a bilingual dictionary or identical character strings, and one unsupervised that does not use any parallel data (see Word Translation without […]