Implementing SimCSE(paper, official repository) using TensorFlow 2 and KR-BERT.

Overview

KR-BERT-SimCSE

Implementing SimCSE(paper, official repository) using TensorFlow 2 and KR-BERT.

Training

Unsupervised

python train_unsupervised.py --mixed_precision

I used Korean Wikipedia Corpus that is divided into sentences in advance. (Check out tfds-korean catalog page for details)

  • Settings
    • KR-BERT character
    • peak learning rate 3e-5
    • batch size 64
    • Total steps: 25,000
    • 0.05 warmup rate, and linear decay learning rate scheduler
    • temperature 0.05
    • evalaute on KLUE STS and KorSTS every 250 steps
    • max sequence length 64
    • Use pooled outputs for training, and [CLS] token's representations for inference

The hyperparameters were not tuned and mostly followed the values in the paper.

Supervised

python train_supervised.py --mixed_precision

I used KorNLI for supervised training. (Check out tfds-korean catalog page)

  • Settings
    • KR-BERT character
    • batch size 128
    • epoch 3
    • peak learning rate 5e-5
    • 0.05 warmup rate, and linear decay learning rate scheduler
    • temperature 0.05
    • evalaute on KLUE STS and KorSTS every 125 steps
    • max sequence length 48
    • Use pooled outputs for training, and [CLS] token's representations for inference

The hyperparameters were not tuned and mostly followed the values in the paper.

Results

KorSTS (dev set results)

model 100 X Spearman correlation
KR-BERT base
SimCSE
unsupervised bi encoding 79.99
KR-BERT base
SimCSE-supervised
trained on KorNLI bi encoding 84.88
SRoBERTa base* unsupervised bi encoding 63.34
SRoBERTa base* trained on KorNLI bi encoding 76.48
SRoBERTa base* trained on KorSTS bi encoding 83.68
SRoBERTa base* trained on KorNLI -> KorSTS bi encoding 83.54
SRoBERTa large* trained on KorNLI bi encoding 77.95
SRoBERTa large* trained on KorSTS bi encoding 84.74
SRoBERTa large* trained on KorNLI -> KorSTS bi encoding 84.21

KorSTS (test set results)

model 100 X Spearman correlation
KR-BERT base
SimCSE
unsupervised bi encoding 73.25
KR-BERT base
SimCSE-supervised
trained on KorNLI bi encoding 80.72
SRoBERTa base* unsupervised bi encoding 48.96
SRoBERTa base* trained on KorNLI bi encoding 74.19
SRoBERTa base* trained on KorSTS bi encoding 78.94
SRoBERTa base* trained on KorNLI -> KorSTS bi encoding 80.29
SRoBERTa large* trained on KorNLI bi encoding 75.46
SRoBERTa large* trained on KorSTS bi encoding 79.55
SRoBERTa large* trained on KorNLI -> KorSTS bi encoding 80.49
SRoBERTa base* trained on KorSTS cross encoding 83.00
SRoBERTa large* trained on KorSTS cross encoding 85.27

KLUE STS (dev set results)

model 100 X Pearson's correlation
KR-BERT base
SimCSE
unsupervised bi encoding 74.45
KR-BERT base
SimCSE-supervised
trained on KorNLI bi encoding 79.42
KR-BERT base* supervised cross encoding 87.50

References

@misc{gao2021simcse,
    title={SimCSE: Simple Contrastive Learning of Sentence Embeddings},
    author={Tianyu Gao and Xingcheng Yao and Danqi Chen},
    year={2021},
    eprint={2104.08821},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@misc{ham2020kornli,
    title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
    author={Jiyeon Ham and Yo Joong Choe and Kyubyong Park and Ilji Choi and Hyungjoon Soh},
    year={2020},
    eprint={2004.03289},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@misc{park2021klue,
    title={KLUE: Korean Language Understanding Evaluation},
    author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jung-Woo Ha and Kyunghyun Cho},
    year={2021},
    eprint={2105.09680},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
Owner
Jeong Ukjae
Jeong Ukjae
SimpleChinese2 集成了许多基本的中文NLP功能,使基于 Python 的中文文字处理和信息提取变得简单方便。

SimpleChinese2 SimpleChinese2 集成了许多基本的中文NLP功能,使基于 Python 的中文文字处理和信息提取变得简单方便。 声明 本项目是为方便个人工作所创建的,仅有部分代码原创。

Ming 30 Dec 02, 2022
Rhyme with AI

Local development Create a conda virtual environment and activate it: conda env create --file environment.yml conda activate rhyme-with-ai Install the

GoDataDriven 28 Nov 21, 2022
Wrapper to display a script output or a text file content on the desktop in sway or other wlroots-based compositors

nwg-wrapper This program is a part of the nwg-shell project. This program is a GTK3-based wrapper to display a script output, or a text file content o

Piotr Miller 94 Dec 27, 2022
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation

Diaformer Diaformer: Automatic Diagnosis via Symptoms Sequence Generation (AAAI 2022) Diaformer is an efficient model for automatic diagnosis via symp

Junying Chen 20 Dec 13, 2022
Fine-tune GPT-3 with a Google Chat conversation history

Google Chat GPT-3 This repo will help you fine-tune GPT-3 with a Google Chat conversation history. The trained model will be able to converse as one o

Nate Baer 7 Dec 10, 2022
Persian-lexicon - A lexicon of 70K unique Persian (Farsi) words

Persian Lexicon This repo uses Uppsala Persian Corpus (UPC) to construct a lexic

Saman Vaisipour 7 Apr 01, 2022
Transformers implementation for Fall 2021 Clinic

Installation Download miniconda3 if not already installed You can check by running typing conda in command prompt. Use conda to create an environment

Aakash Tripathi 1 Oct 28, 2021
KLUE-baseline contains the baseline code for the Korean Language Understanding Evaluation (KLUE) benchmark.

KLUE Baseline Korean(한국어) KLUE-baseline contains the baseline code for the Korean Language Understanding Evaluation (KLUE) benchmark. See our paper fo

74 Dec 13, 2022
Code for Findings at EMNLP 2021 paper: "Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning"

Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning This repo is for Findings at EMNLP 2021 paper: Learn Cont

INK Lab @ USC 6 Sep 02, 2022
Weird Sort-and-Compress Thing

Weird Sort-and-Compress Thing A weird integer sorting + compression algorithm inspired by a conversation with Luthingx (it probably already exists by

Douglas 1 Jan 03, 2022
Refactored version of FastSpeech2

Refactored version of FastSpeech2. An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

ILJI CHOI 10 May 26, 2022
Grover is a model for Neural Fake News -- both generation and detectio

Grover is a model for Neural Fake News -- both generation and detection. However, it probably can also be used for other generation tasks.

Rowan Zellers 856 Dec 24, 2022
A Practitioner's Guide to Natural Language Processing

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, Text

Dipanjan (DJ) Sarkar 1.5k Jan 03, 2023
Code-autocomplete, a code completion plugin for Python

Code AutoComplete code-autocomplete, a code completion plugin for Python.

xuming 13 Jan 07, 2023
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

ELECTRA Introduction ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using

Google Research 2.1k Dec 28, 2022
Cải thiện Elasticsearch trong bài toán semantic search sử dụng phương pháp Sentence Embeddings

Cải thiện Elasticsearch trong bài toán semantic search sử dụng phương pháp Sentence Embeddings Trong bài viết này mình sẽ sử dụng pretrain model SimCS

Vo Van Phuc 18 Nov 25, 2022
CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus

CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus CVSS is a massively multilingual-to-English speech-to-speech translation corpus, co

Google Research Datasets 118 Jan 06, 2023
The code from the whylogs workshop in DataTalks.Club on 29 March 2022

whylogs Workshop The code from the whylogs workshop in DataTalks.Club on 29 March 2022 whylogs - The open source standard for data logging (Don't forg

DataTalksClub 12 Sep 05, 2022
jiant is an NLP toolkit

🚨 Update 🚨 : As of 2021/10/17, the jiant project is no longer being actively maintained. This means there will be no plans to add new models, tasks,

ML² AT CILVR 1.5k Dec 28, 2022
Natural Language Processing with transformers

we want to create a repo to illustrate usage of transformers in chinese

Datawhale 763 Dec 27, 2022