[NAACL & ACL 2021] SapBERT: Self-alignment pretraining for BERT.

Overview

SapBERT: Self-alignment pretraining for BERT

This repo holds code for the SapBERT model presented in our NAACL 2021 paper: Self-Alignment Pretraining for Biomedical Entity Representations [arxiv]; and our ACL 2021 paper: Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking [PDF].

front-page-graph

Huggingface Models

[SapBERT]

Standard SapBERT as described in [Liu et al., NAACL 2021]. Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. Use [CLS] (before pooler) as the representation of the input.

[SapBERT-XLMR]

Cross-lingual SapBERT as described in [Liu et al., ACL 2021]. Trained with UMLS 2020AB (all languages), using xlm-roberta-base as the base model. Use [CLS] (before pooler) as the representation of the input.

[SapBERT-mean-token]

Same as the standard SapBERT but trained with mean-pooling instead of [CLS] representations.

Environment

The code is tested with python 3.8, torch 1.7.0 and huggingface transformers 4.4.2. Please view requirements.txt for more details.

Train SapBERT

Prepare training data as insrtructed in data/generate_pretraining_data.ipynb.

Run:

cd umls_pretraining
./pretrain.sh 0,1 

where 0,1 specifies the GPU devices.

Evaluate SapBERT

Please view evaluation/README.md for details.

Citations

@article{liu2021self,
	title={Self-Alignment Pretraining for Biomedical Entity Representations},
	author={Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel},
	journal={arXiv preprint arXiv:2010.11784},
	year={2020}
}

Acknowledgement

Parts of the code are modified from BioSyn. We appreciate the authors for making BioSyn open-sourced.

License

SapBERT is MIT licensed. See the LICENSE file for details.

Owner
Cambridge Language Technology Lab
Cambridge Language Technology Lab
Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

3 May 12, 2022
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising By Tengfei Liang, Yi Jin, Yidong Li, Tao Wang. Th

workingcoder 115 Jan 05, 2023
A curated list of awesome Model-Based RL resources

Awesome Model-Based Reinforcement Learning This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository w

OpenDILab 427 Jan 03, 2023
Plotting points that lie on the intersection of the given curves using gradient descent.

Plotting intersection of curves using gradient descent Webapp Link --- What's the app about Why this app Plotting functions and their intersection. A

Divakar Verma 2 Jan 09, 2022
Unofficial implementation of HiFi-GAN+ from the paper "Bandwidth Extension is All You Need" by Su, et al.

HiFi-GAN+ This project is an unoffical implementation of the HiFi-GAN+ model for audio bandwidth extension, from the paper Bandwidth Extension is All

Brent M. Spell 134 Dec 30, 2022
offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

PeterPu 76 Dec 08, 2022
PyTorch implementation of Pay Attention to MLPs

gMLP PyTorch implementation of Pay Attention to MLPs. Quickstart Clone this repository. git clone https://github.com/jaketae/g-mlp.git Navigate to th

Jake Tae 34 Dec 13, 2022
PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

DeepLab with PyTorch This is an unofficial PyTorch implementation of DeepLab v2 [1] with a ResNet-101 backbone. COCO-Stuff dataset [2] and PASCAL VOC

Kazuto Nakashima 995 Jan 08, 2023
[CVPR'22] COAP: Learning Compositional Occupancy of People

COAP: Compositional Articulated Occupancy of People Paper | Video | Project Page This is the official implementation of the CVPR 2022 paper COAP: Lear

Marko Mihajlovic 111 Dec 11, 2022
CycleTransGAN-EVC: A CycleGAN-based Emotional Voice Conversion Model with Transformer

CycleTransGAN-EVC CycleTransGAN-EVC: A CycleGAN-based Emotional Voice Conversion Model with Transformer Demo emotion CycleTransGAN CycleTransGAN Cycle

24 Dec 15, 2022
How to Learn a Domain Adaptive Event Simulator? ACM MM, 2021

LETGAN How to Learn a Domain Adaptive Event Simulator? ACM MM 2021 Running Environment: pytorch=1.4, 1 NVIDIA-1080TI. More details can be found in pap

CVTEAM 4 Sep 20, 2022
Building Ellee — A GPT-3 and Computer Vision Powered Talking Robotic Teddy Bear With Human Level Conversation Intelligence

Using an object detection and facial recognition system built on MobileNetSSDV2 and Dlib and running on an NVIDIA Jetson Nano, a GPT-3 model, Google Speech Recognition, Amazon Polly and servo motors,

24 Oct 26, 2022
Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Christopher T. Chubb 35 Dec 21, 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da

Sanyam Kapoor 18 Nov 17, 2022
Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh

generate_cloud_points Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh. Run python disp_mesh.py Or you

Peng Yu 2 Dec 24, 2021
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
Unofficial implementation of One-Shot Free-View Neural Talking Head Synthesis

face-vid2vid Usage Dataset Preparation cd datasets wget https://yt-dl.org/downloads/latest/youtube-dl -O youtube-dl chmod a+rx youtube-dl python load_

worstcoder 68 Dec 30, 2022
Libraries, tools and tasks created and used at DeepMind Robotics.

dm_robotics: Libraries, tools, and tasks created and used for Robotics research at DeepMind. Package overview Package Summary Transformations Rigid bo

DeepMind 273 Jan 06, 2023
PyTorch implementation of Convolutional Neural Fabrics http://arxiv.org/abs/1606.02492

PyTorch implementation of Convolutional Neural Fabrics arxiv:1606.02492 There are some minor differences: The raw image is first convolved, to obtain

Anuvabh Dutt 25 Dec 22, 2021
[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

Discriminative Region-based Multi-Label Zero-Shot Learning (ICCV 2021) [arXiv][Project page coming soon] Sanath Narayan*, Akshita Gupta*, Salman Kh

Akshita Gupta 54 Nov 21, 2022