Paraphrastic Representations at Scale

Code to train models from "Paraphrastic Representations at Scale".

The code is written in Python 3.7 and requires H5py, jieba, numpy, scipy, sentencepiece, sacremoses, and PyTorch >= 1.0 libraries. These can be insalled with the following command:

pip install -r requirements.txt

To get started, download the data files used for training from http://www.cs.cmu.edu/~jwieting and download the STS evaluation data:

wget http://phontron.com/data/paraphrase-at-scale.zip
unzip paraphrase-at-scale.zip
rm paraphrase-at-scale.zip
wget http://www.cs.cmu.edu/~jwieting/STS.zip .
unzip STS.zip
rm STS.zip

If you use our code, models, or data for your work please cite:

@article{wieting2021paraphrastic,
    title={Paraphrastic Representations at Scale},
    author={Wieting, John and Gimpel, Kevin and Neubig, Graham and Berg-Kirkpatrick, Taylor},
    journal={arXiv preprint arXiv:2104.15114},
    year={2021}
}

@inproceedings{wieting19simple,
    title={Simple and Effective Paraphrastic Similarity from Parallel Translations},
    author={Wieting, John and Gimpel, Kevin and Neubig, Graham and Berg-Kirkpatrick, Taylor},
    booktitle={Proceedings of the Association for Computational Linguistics},
    url={https://arxiv.org/abs/1909.13872},
    year={2019}
}

To embed a list of sentences:

python -u embed_sentences.py --sentence-file paraphrase-at-scale/example-sentences.txt --load-file paraphrase-at-scale/model.para.lc.100.pt  --sp-model paraphrase-at-scale/paranmt.model --output-file sentence_embeds.np --gpu 0

To score a list of sentence pairs:

python -u score_sentence_pairs.py --sentence-pair-file paraphrase-at-scale/example-sentences-pairs.txt --load-file paraphrase-at-scale/model.para.lc.100.pt  --sp-model paraphrase-at-scale/paranmt.model --gpu 0

To train a model (for example, on ParaNMT):

python -u main.py --outfile model.para.out --lower-case 1 --tokenize 0 --data-file paraphrase-at-scale/paranmt.sim-low=0.4-sim-high=1.0-ovl=0.7.final.h5 \
       --model avg --dim 1024 --epochs 25 --dropout 0.0 --sp-model paraphrase-at-scale/paranmt.model --megabatch-size 100 --save-every-epoch 1 --gpu 0 --vocab-file paraphrase-at-scale/paranmt.sim-low=0.4-sim-high=1.0-ovl=0.7.final.vocab

To download and preprocess raw data for training models (both bilingual and ParaNMT), see preprocess/bilingual and preprocess/paranmt.

Code to train models from "Paraphrastic Representations at Scale".

Related tags

Overview

Paraphrastic Representations at Scale

Owner

John Wieting

GAN-based Matrix Factorization for Recommender Systems

Semantic Bottleneck Scene Generation

A machine learning malware analysis framework for Android apps.

Taming Transformers for High-Resolution Image Synthesis

Code for "Unsupervised State Representation Learning in Atari"

[CVPR 2022] Pytorch implementation of "Templates for 3D Object Pose Estimation Revisited: Generalization to New objects and Robustness to Occlusions" paper

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CenterPoint 3D Object Detection and Tracking using center points in the bird-eye view.

Official repository of the paper Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

Rank 1st in the public leaderboard of ScanRefer (2021-03-18)

Reusable constraint types to use with typing.Annotated

PyTorch code of my WACV 2022 paper Improving Model Generalization by Agreement of Learned Representations from Data Augmentation

MG-GCN: Scalable Multi-GPU GCN Training Framework

ScaleNet: A Shallow Architecture for Scale Estimation

Ejemplo Algoritmo Viterbi - Example of a Viterbi algorithm applied to a hidden Markov model on DNA sequence

Learning Energy-Based Models by Diffusion Recovery Likelihood

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

Visual Question Answering in Pytorch

A Closer Look at Structured Pruning for Neural Network Compression

Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment