A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.

Related tags

Text Data & NLPKGEval
Overview

KGEval

A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.

The framework and experimental results are described in Ben Rim et al. 2021 (Outstanding Paper Award, AKBC 2021).

Instructions

Create a virtual environment

virtualenv -p python3.6 eval_env
source eval_env/bin/activate
pip install -r requirements.txt

Download data

In the main folder, run:

source data/download.sh

Download model

If you want to test the framework immediately, you can download pre-trained Pykeen models by running:

source download_models.sh

Generate behavioral tests

Symmetry Tests

Can choose --dataset FB15K237, WN18RR, YAGO310

python tests/run.py --dataset FB15K237 --mode generate --capability symmetry

This should result into the following output, and the files for each test set will be added under behavioral_tests\dataset\symmetry:

2021-10-03 23:37:35,060 - [INFO] - Preparing test sets for the dataset FB15K237
2021-10-03 23:37:37,621 - [INFO] - ########################## <----TRAIN---> ############################
2021-10-03 23:37:37,621 - [INFO] - 0 repetitions removed
2021-10-03 23:37:37,621 - [INFO] - 272115 triples remaining in train set
2021-10-03 23:37:37,621 - [INFO] - 6778 symmetric triples found in train set
2021-10-03 23:37:37,786 - [INFO] - ########################## <----TEST---> ############################
2021-10-03 23:37:37,786 - [INFO] - 0 repetitions removed
2021-10-03 23:37:37,786 - [INFO] - 20466 triples remaining in test set
2021-10-03 23:37:37,786 - [INFO] - 113 symmetric triples found in test set
2021-10-03 23:37:37,806 - [INFO] - ########################## <----VALID---> ############################
2021-10-03 23:37:37,806 - [INFO] - 0 repetitions removed
2021-10-03 23:37:37,806 - [INFO] - 17535 triples remaining in valid set
2021-10-03 23:37:37,806 - [INFO] - 113 symmetric triples found in valid set
2021-10-03 23:37:39,106 - [INFO] - #################### <---TEST SET 1: MEMORIZATION ---> ##########################
2021-10-03 23:37:39,106 - [INFO] - There are 5470 entries in the memorization set (occur in both directions)
2021-10-03 23:37:39,106 - [INFO] - #################### <---TEST SET 2: ONE DIRECTION SEEN ---> ##########################
2021-10-03 23:37:39,106 - [INFO] - There are 1308 entries not shown in both directions (to be reversed for testing)
2021-10-03 23:37:39,836 - [INFO] - #################### <--- SYMMETRIC RELATIONS ---> ##########################
2021-10-03 23:37:39,836 - [INFO] - TRAIN SET contains 6778 symmetric entries
2021-10-03 23:37:39,836 - [INFO] - TEST SET contains  113 symmetric entries with 113 not in training
2021-10-03 23:37:39,836 - [INFO] - VALID SET contains 113 symmetric entries with 113 not in training
2021-10-03 23:37:39,839 - [INFO] - #################### <---TEST SET 3: UNSEEN INSTANCES ---> ##########################
2021-10-03 23:37:39,840 - [INFO] - There are 226 entries that are not seen in any direction in training
2021-10-03 23:37:40,267 - [INFO] - #################### <---TEST SET 4: ASYMMETRY ---> ##########################
2021-10-03 23:37:40,267 - [INFO] - There are 3000 asymmetric entries in test set added to test 4

Hierarchy Tests

Only available for FB15K237 dataset

python tests/run.py --dataset FB15K237 --mode generate --capability hierarchy

The output should be and will be available under behavioral_tests/dataset/hierarchy/, the naming of the files corresponds to triples where the tail belongs to a specified level. For example, 1.txt contains triples where the tail has a type of level 1 in the entity type hierarchy :

2021-10-04 01:38:13,517 - [INFO] - Results of Hierarchy Behavioral Tests for FB15K237
2021-10-04 01:38:20,367 - [INFO] - <--------------- Entity Hiararchy statistics ----------------->
2021-10-04 01:38:20,568 - [INFO] - Level 0 contains 1 types and 3415 triples
2021-10-04 01:38:20,887 - [INFO] - Level 1 contains 66 types and 2006 triples
2021-10-04 01:38:20,900 - [INFO] - Level 2 contains 136 types and 4273 triples
2021-10-04 01:38:20,913 - [INFO] - Level 3 contains 213 types and 3560 triples
2021-10-04 01:38:20,923 - [INFO] - Level 4 contains 262 types and 3369 triples

Run Tests (pykeen models)

Symmetry behavioral tests on distmult or rotate:

python tests/run.py --dataset FB15K237 --mode test --model_name rotate

The output will be printed as shown below, and will also be available in the results folder under dataset/symmetry:

2021-10-04 14:00:57,100 - [INFO] - Starting test1 with rotate model
2021-10-04 14:03:23,249 - [INFO] - On test1, MR: 1.2407678244972578, MRR: 0.9400152688974949, [email protected]: 0.9014624953269958, [email protected]: 0.988482654094696, [email protected]: 0.9965264797210693
2021-10-04 14:03:23,249 - [INFO] - Starting test2 with rotate model
2021-10-04 14:04:15,614 - [INFO] - On test2, MR: 23.446483180428135, MRR: 0.4409348919640765, [email protected]: 0.30351680517196655, [email protected]: 0.5894495248794556, [email protected]: 0.7025994062423706
2021-10-04 14:04:15,614 - [INFO] - Starting test3 with rotate model
2021-10-04 14:04:25,364 - [INFO] - On test3, MR: 1018.9469026548672, MRR: 0.04786047740344238, [email protected]: 0.008849557489156723, [email protected]: 0.06194690242409706, [email protected]: 0.12389380484819412
2021-10-04 14:04:25,365 - [INFO] - Starting test4 with rotate model
2021-10-04 14:05:38,900 - [INFO] - On test4, MR: 4901.459, MRR: 0.07606098649786266, [email protected]: 0.9496666789054871, [email protected]: 0.893666684627533, [email protected]: 0.8823333382606506

Hierarchy behavioral tests on distmult or rotate:

   python tests/run.py --dataset FB15K237 --mode test --capability hierarchy --model_name rotate

Run Tests on other models and other frameworks

(To be added)

Owner
NEC Laboratories Europe
Research software developed at NEC Laboratories Europe
NEC Laboratories Europe
GVT is a generic translation tool for parts of text on the PC screen with Text to Speak functionality.

GVT is a generic translation tool for parts of text on the PC screen with Text to Speech functionality. I wanted to create it because the existing tools that I experimented with did not satisfy me in

Nuked 1 Aug 21, 2022
The (extremely) naive sentiment classification function based on NBSVM trained on wisesight_sentiment

thai_sentiment The naive sentiment classification function based on NBSVM trained on wisesight_sentiment วิธีติดตั้ง pip install thai_sentiment==0.1.3

Charin 7 Dec 08, 2022
Unofficial Implementation of Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration

Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration This repo contains only model Implementation of Zero-Shot Text-to-Speech for Text

Rishikesh (ऋषिकेश) 33 Sep 22, 2022
multi-label,classifier,text classification,多标签文本分类,文本分类,BERT,ALBERT,multi-label-classification,seq2seq,attention,beam search

multi-label,classifier,text classification,多标签文本分类,文本分类,BERT,ALBERT,multi-label-classification,seq2seq,attention,beam search

hellonlp 30 Dec 12, 2022
Turn clang-tidy warnings and fixes to comments in your pull request

clang-tidy pull request comments A GitHub Action to post clang-tidy warnings and suggestions as review comments on your pull request. What platisd/cla

Dimitris Platis 30 Dec 13, 2022
HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools

HuggingSound HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools. I have no intention of building a very complex tool here.

Jonatas Grosman 247 Dec 26, 2022
FireFlyer Record file format, writer and reader for DL training samples.

FFRecord The FFRecord format is a simple format for storing a sequence of binary records developed by HFAiLab, which supports random access and Linux

77 Jan 04, 2023
A Python/Pytorch app for easily synthesising human voices

Voice Cloning App A Python/Pytorch app for easily synthesising human voices Documentation Discord Server Video guide Voice Sharing Hub FAQ's System Re

Ben Andrew 840 Jan 04, 2023
Conditional probing: measuring usable information beyond a baseline

Conditional probing: measuring usable information beyond a baseline

John Hewitt 20 Dec 15, 2022
PyTorch implementation of Tacotron speech synthesis model.

tacotron_pytorch PyTorch implementation of Tacotron speech synthesis model. Inspired from keithito/tacotron. Currently not as much good speech quality

Ryuichi Yamamoto 279 Dec 09, 2022
LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation

LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation Tasks | Datasets | LongLM | Baselines | Paper Introduction LOT is a ben

46 Dec 28, 2022
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

⚠️ Checkout develop branch to see what is coming in pyannote.audio 2.0: a much smaller and cleaner codebase Python-first API (the good old pyannote-au

pyannote 2.2k Jan 09, 2023
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
Treemap visualisation of Maya scene files

Ever wondered which nodes are responsible for that 600 mb+ Maya scene file? Features Fast, resizable UI Parsing at 50 mb/sec Dependency-free, single-f

Marcus Ottosson 76 Nov 12, 2022
Modular and extensible speech recognition library leveraging pytorch-lightning and hydra.

Lightning ASR Modular and extensible speech recognition library leveraging pytorch-lightning and hydra What is Lightning ASR • Installation • Get Star

Soohwan Kim 40 Sep 19, 2022
Python functions for summarizing and improving voice dictation input.

Helpmespeak Help me speak uses Python functions for summarizing and improving voice dictation input. Get started with OpenAI gpt-3 OpenAI is a amazing

Margarita Humanitarian Foundation 6 Dec 17, 2022
Vad-sli-asr - A Python scripts for a speech processing pipeline with Voice Activity Detection (VAD)

VAD-SLI-ASR Python scripts for a speech processing pipeline with Voice Activity

Dynamics of Language 14 Dec 09, 2022
Google and Stanford University released a new pre-trained model called ELECTRA

Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants. For furth

Yiming Cui 1.2k Dec 30, 2022
Pipeline for chemical image-to-text competition

BMS-Molecular-Translation Introduction This is a pipeline for Bristol-Myers Squibb – Molecular Translation by Vadim Timakin and Maksim Zhdanov. We got

Maksim Zhdanov 7 Sep 20, 2022
RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2

RoNER RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2. It is meant to be an easy to use, hi

Stefan Dumitrescu 9 Nov 07, 2022