git《Commonsense Knowledge Base Completion with Structural and Semantic Context》(AAAI 2020) GitHub: [fig1]

Overview

Commonsense Knowledge Base Completion with Structural and Semantic Context

Code for the paper Commonsense Knowledge Base Completion with Structural and Semantic Context.

Bibtex

@article{malaviya2020commonsense,
  title={Commonsense Knowledge Base Completion with Structural and Semantic Context},
  author={Malaviya, Chaitanya and Bhagavatula, Chandra and Bosselut, Antoine and Choi, Yejin},
  journal={Proceedings of the 34th AAAI Conference on Artificial Intelligence},
  year={2020}
}

Requirements

  • PyTorch
  • Run pip install -r requirements.txt to install the required packages.

Dataset

The ATOMIC dataset used in this paper is available here and the ConceptNet graph is available here. For convenience, both the pre-processed version of ATOMIC and ConceptNet used in the experiments are provided at this link.

Note: The ATOMIC dataset was pre-processed to canonicalize person references and remove punctuations (described in preprocess_atomic.py.

Note: The original evaluation sets provided in the ConceptNet dataset contain correct as well as incorrect tuples for evaluating binary classification accuracy. valid.txt in data/conceptnet is the concatenation of the correct tuples from the two development sets provided in the original dataset while test.txt is the set of correct tuples from the original test set.

Training

To train a model, run the following command:

python -u src/run_kbc_subgraph.py --dataset conceptnet --evaluate-every 10 --n-layers 2 --graph-batch-size 60000 --sim_relations --bert_concat

This trains the model and saves the model under the saved_models directory.

Language Model Fine-tuning

In this work, we use representations from a BERT model fine-tuned to the language of the nodes in the knowledge graph.

The script to fine-tune BERT as a language model on the two knowledge graphs is present in the lm_finetuning/ directory. For example, here is a command to fine-tune BERT as a language model on ConceptNet:

python lm_finetuning/simple_lm_finetuning.py --train_corpus {CONCEPTNET_TRAIN_CORPUS} --bert_model bert-large-uncased --output_dir {OUTPUT_DIR}

Pre-Trained Models

We provide the fine-tuned BERT models and pre-computed BERT embeddings for both ConceptNet and ATOMIC at this link. If you unzip the downloaded file in the root directory of the repository, the training script will load the embeddings.

We also provide the pre-trained KB completion models for both datasets for ease of use. Link to Conceptnet model and ATOMIC model.

Evaluation

To evaluate a trained model, and get predictions, provide the model path to the --load_model argument and use the --eval_only argument. For example, to evaluate the pre-trained ConceptNet model provided above, use the following command:

CUDA_VISIBLE_DEVICES={GPU_ID} python src/run_kbc_subgraph.py --dataset conceptnet --sim_relations --bert_concat --use_bias --load_model {PATH_TO_PRETRAINED_MODEL} --eval_only --write_results

This will load the pre-trained model, and evaluate it on the validation and test set. The predictions are saved to ./topk_results.json.

Similarly, to evaluate the trained model on ATOMIC, use the following command:

CUDA_VISIBLE_DEVICES={GPU_ID} python src/run_kbc_subgraph.py --dataset atomic --sim_relations --use_bias --load_model {PATH_TO_PRETRAINED_MODEL} --eval_only --write_results

Please email me at [email protected] for any questions or comments.

The code for our CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp.

PISE The code for our CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp. Requirement conda create -n pise pyt

jinszhang 110 Nov 21, 2022
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

EMI-Group 175 Dec 30, 2022
Taming Transformers for High-Resolution Image Synthesis

Taming Transformers for High-Resolution Image Synthesis CVPR 2021 (Oral) Taming Transformers for High-Resolution Image Synthesis Patrick Esser*, Robin

CompVis Heidelberg 3.5k Jan 03, 2023
pq is a jq-like Pickle file viewer

pq PQ is a jq-like viewer/processing tool for pickle files. howto # pq '' file.pkl {'other': 456, 'test': 123} # pq 'table' file.pkl |other|test| | 45

3 Mar 15, 2022
TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

Aritra Roy Gosthipaty 23 Dec 24, 2022
Mesh Graphormer is a new transformer-based method for human pose and mesh reconsruction from an input image

MeshGraphormer ✨ ✨ This is our research code of Mesh Graphormer. Mesh Graphormer is a new transformer-based method for human pose and mesh reconsructi

Microsoft 251 Jan 08, 2023
EGNN - Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch

EGNN - Pytorch Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch. May be eventually used for Alphafold2 replication. This

Phil Wang 259 Jan 04, 2023
Clinica is a software platform for clinical research studies involving patients with neurological and psychiatric diseases and the acquisition of multimodal data

Clinica Software platform for clinical neuroimaging studies Homepage | Documentation | Paper | Forum | See also: AD-ML, AD-DL ClinicaDL About The Proj

ARAMIS Lab 165 Dec 29, 2022
Code repository for the paper Computer Vision User Entity Behavior Analytics

Computer Vision User Entity Behavior Analytics Code repository for "Computer Vision User Entity Behavior Analytics" Code Description dataset.csv As di

Sameer Khanna 2 Aug 20, 2022
Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning"

CMSF Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning" Requirements Python = 3.7.6 PyTorch

4 Nov 25, 2022
Unicorn can be used for performance analyses of highly configurable systems with causal reasoning

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning. Users or developers can query Unicorn for a performance task.

AISys Lab 27 Jan 05, 2023
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch

Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b

Thuy Ng 474 Dec 19, 2022
An abstraction layer for mathematical optimization solvers.

MathOptInterface Documentation Build Status Social An abstraction layer for mathematical optimization solvers. Replaces MathProgBase. Citing MathOptIn

JuMP-dev 284 Jan 04, 2023
Flexible-Modal Face Anti-Spoofing: A Benchmark

Flexible-Modal FAS This is the official repository of "Flexible-Modal Face Anti-

Zitong Yu 22 Nov 10, 2022
yolov5目标检测模型的知识蒸馏(基于响应的蒸馏)

代码地址: https://github.com/Sharpiless/yolov5-knowledge-distillation 教师模型: python train.py --weights weights/yolov5m.pt \ --cfg models/yolov5m.ya

52 Dec 04, 2022
Spam your friends and famly and when you do your famly will disown you and you will have no friends.

SpamBot9000 Spam your friends and family and when you do your family will disown you and you will have no friends. Terms of Use Disclaimer: Please onl

DJ15 0 Jun 09, 2022
An implementation of EWC with PyTorch

EWC.pytorch An implementation of Elastic Weight Consolidation (EWC), proposed in James Kirkpatrick et al. Overcoming catastrophic forgetting in neural

Ryuichiro Hataya 166 Dec 22, 2022
All of the figures and notebooks for my deep learning book, for free!

"Deep Learning - A Visual Approach" by Andrew Glassner This is the official repo for my book from No Starch Press. Ordering the book My book is called

Andrew Glassner 227 Jan 04, 2023
This repo provides function call to track multi-objects in videos

Custom Object Tracking Introduction This repo provides function call to track multi-objects in videos with a given trained object detection model and

Jeff Lo 51 Nov 22, 2022
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

SimMIM By Zhenda Xie*, Zheng Zhang*, Yue Cao*, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai and Han Hu*. This repo is the official implementation of

Microsoft 674 Dec 26, 2022