KEPLER
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Tsinghua University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),HEC,CIFAR AI Research,Princeton University,University of Montreal / Université de Montréal
- Organisation type
- Academia,Academia,Academia,Research collective,Academia,Academia
- Country
- China, Canada, France, United States of America
- Published
- 23 November 2020
- Authors
- Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhiyuan Liu, Juanzi Li, and Jian Tang.
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Relation extraction
- Approach
- Self-supervised learning
- Base model
- RoBERTa Base
- Numerical format
- FP16
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 125M
- Training data
- 3,533,640,000 tokens
For BookCorpus + English Wikipedia: 800M + 2500M For Wikidata5M: 20614279 See table 1. Contains "entities", "relations", and "triplets"
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.7 × 10²¹ FLOP
- How it was established
- Hardware
From author communication "About 128 GPU-days using Nvidia V100 (16GB). " precision: float16 V100 GPU for float16: 28000000000000 (2.8E+13) 0.4 * 28TFLOP/s * 128 GPU-days * 24h/day * 3600s/h = 1.24E+20 "and use the released roberta.base parameters for initialization, which is a common practice to save pre-training time" Roberta base FLOP: 1.536e+21 Total:1.660000e+21
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
MIT License, includes train code https://github.com/THU-KEG/KEPLER
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 819
"Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks"
Sources
Where this record came from and when it was last checked.
- Reference
- KEPLER: A Unified Model for Knowledge Embedding and Pre- trained Language Representation.
- Last updated
- 25 May 2026
What the numbers mean
About this model
KEPLER was published by Tsinghua University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),HEC,CIFAR AI Research,Princeton University,University of Montreal / Université de Montréal, in the country recorded as China, during November 2020. The category the publisher falls under is academia,Academia,Academia,Research collective,Academia,Academia.
It works in the domain of Language, and is recorded as performing the task of relation extraction.
It builds on RoBERTa Base. That is why it shares the base model's general shape and size.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training it took a computation budget of roughly 1.7 × 10²¹ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 3,533,640,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
KEPLER — common questions
KEPLER— when was it released?
It was published in November 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
KEPLER— what is it used for?
It works in the domain of Language, and is recorded as handling the task of relation extraction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
KEPLER— how much compute was used to train it?
Training consumed around 1.7 × 10²¹ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
KEPLER— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
KEPLER— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
KEPLER— how many parameters does it have?
It has a parameter count of 125M. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
KEPLER— who created it?
It was published by Tsinghua University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),HEC,CIFAR AI Research,Princeton University,University of Montreal / Université de Montréal, based in China, an organisation categorised as academia,Academia,Academia,Research collective,Academia,Academia.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.