CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning

This repository contains the code and relevant instructions of CRLT.

Overview

The goal of CRLT is to provide an out-of-the-box toolkit for contrastive learning. Users only need to provide unlabeled data and edit a configuration file in the format of JSON, and then they can quickly train, use and evaluate representation learning models. CRLT consists of 6 critical modules, including data synthesis, negative sampling, representation encoders, learning paradigm, optimizing strategy and model evaluation. For each module, CRLT provides various popular implementations and therefore different kinds of CL architectures can be easily constructed using CRLT.

Installation

Requirements

First, run the following script to install the relevant dependencies

conda env create -f requirements.yaml

Then, install PyTorch by following the instructions from the official website. Please use the correct 1.10 version corresponding to your platforms/CUDA versions. PyTorch version higher than 1.10 should also work. For example, if you use Linux and CUDA10.2, install PyTorch by the following command,

conda activate crlt
conda install pytorch==1.10.0 cudatoolkit=10.2 -c pytorch

The evaluation code for sentence embeddings is based on a modified version of SentEval. It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation. See SimCSE for more details.

Before training, please download the relevent datasets by running:

cd utils/SentEval/data/downstream/
bash download.sh

Then, running the command to install the SentEval toolkit:

cd utils/SentEval
python setyp.py install

Getting Started

Data

For unsupervised training, we use sentences from English Wikipedia provided by SimCSE, and the relevant dataset should be download and moved to the data/wiki folder:

When training, CRLT use the dev set of STSB task to evaluate the model, so the used file need to be download to data/STSB folder:

Training

GUI

We provide example training scripts for SimCSE (the unsupervised version) by running:

conda activate crlt
python app.py

After editing the training parameters, users click the RUN button and will get the evaluation result on the same page.

Terminal

Rather than training with the web GUI, users can also train by running:

python main.py examples/simcse.json

Using different types of devices or different versions of CUDA/other softwares may lead to slightly different performance:

Bugs or questions?

If you have any questions related to the code or the usage, feel free to email [email protected]. If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!