Assessing syntactic abilities of BERT

Overview

BERT-Syntax

Assesing the syntactic abilities of BERT.

What

Evaluate Google's BERT-Base and BERT-Large models on the syntactic agreement datasets from Linzen, Goldberg and Dupoux 2016 and Marvin and Linzen 2018 and Gulordava et al 2018.

This is quite messy, as I hacked it together between things here and there. But I also believe it is accurate. This lists the data files and shows how to run the evaluation. For more details and results, see the arxiv report.

Data Files

Data taken from the github repos of Linzen, Goldberg and Dupoux (LGD), Marvin and Linzen (ML), and Gulordava et al.

File Description
marvin_linzen_dataset.tsv stimuli from Marvin and Linzen. I dumped it from the pickle files of ML
wiki.vocab from LGD, used for verb inflections (wiki.vocab)
lgd_dataset.tsv processed data from LGD
generated.tab data from Gulordava et al (generated.tab)

lgd_dataset.tsv is created by

wget http://tallinzen.net/media/rnn_agreement/agr_50_mostcommon_10K.tsv.gz
gunzip agr_50_mostcommon_10K.tsv.gz
python make_linzen_goldberg_testset.py > lgd_dataset.tsv

Obtaining the results

pip install pytorch_pretrained_bert

python eval_bert.py > results/lgd_results_large.txt
python eval_bert.py base > results/lgd_results_base.txt
python eval_bert.py marvin > results/marvin_results_large.txt
python eval_bert.py marvin base > results/marvin_results_base.txt
python eval_bert.py gul > results/gulordava_results_large.txt
python eval_bert.py gul base > results/gulordava_results_base.txt

Generating tables (for the PDF)

python gen_marvin_tbl.py 
python gen_lgd_tbl.py
python gen_gul_tbl.py
Owner
Yoav Goldberg
Yoav Goldberg
Official Pytorch implementation of Meta Internal Learning

Official Pytorch implementation of Meta Internal Learning

10 Aug 24, 2022
This is the face keypoint train code of project face-detection-project

face-key-point-pytorch 1. Data structure The structure of landmarks_jpg is like below: |--landmarks_jpg |----AFW |------AFW_134212_1_0.jpg |------AFW_

I‘m X 3 Nov 27, 2022
I decide to sync up this repo and self-critical.pytorch. (The old master is in old master branch for archive)

An Image Captioning codebase This is a codebase for image captioning research. It supports: Self critical training from Self-critical Sequence Trainin

Ruotian(RT) Luo 1.3k Dec 31, 2022
Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.

CLIP-Guided-Diffusion Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab. Original colab notebooks by Ka

Nerdy Rodent 336 Dec 09, 2022
Omniscient Video Super-Resolution

Omniscient Video Super-Resolution This is the official code of OVSR (Omniscient Video Super-Resolution, ICCV 2021). This work is based on PFNL. Datase

36 Oct 27, 2022
[CVPR 2022] Structured Sparse R-CNN for Direct Scene Graph Generation

Structured Sparse R-CNN for Direct Scene Graph Generation Our paper Structured Sparse R-CNN for Direct Scene Graph Generation has been accepted by CVP

Multimedia Computing Group, Nanjing University 44 Dec 23, 2022
A system for quickly generating training data with weak supervision

Programmatically Build and Manage Training Data Announcement The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI applicat

Snorkel Team 5.4k Jan 02, 2023
Welcome to The Eigensolver Quantum School, a quantum computing crash course designed by students for students.

TEQS Welcome to The Eigensolver Quantum School, a crash course designed by students for students. The aim of this program is to take someone who has n

The Eigensolvers 53 May 18, 2022
[ICME 2021 Oral] CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning This repository is the official PyTorch implementation of CORE-Text, a

Jingyang Lin 18 Aug 11, 2022
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Dec 31, 2022
DenseNet Implementation in Keras with ImageNet Pretrained Models

DenseNet-Keras with ImageNet Pretrained Models This is an Keras implementation of DenseNet with ImageNet pretrained weights. The weights are converted

Felix Yu 568 Oct 31, 2022
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

1.1k Jan 03, 2023
📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Rahul Vigneswaran 1 Jan 17, 2022
Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

1 Oct 11, 2021
AutoDeeplab / auto-deeplab / AutoML for semantic segmentation, implemented in Pytorch

AutoML for Image Semantic Segmentation Currently this repo contains the only working open-source implementation of Auto-Deeplab which, by the way out-

AI Necromancer 299 Dec 17, 2022
Tensorflow implementation of ID-Unet: Iterative Soft and Hard Deformation for View Synthesis.

ID-Unet: Iterative-view-synthesis(CVPR2021 Oral) Tensorflow implementation of ID-Unet: Iterative Soft and Hard Deformation for View Synthesis. Overvie

17 Aug 23, 2022
Learning to Map Large-scale Sparse Graphs on Memristive Crossbar

Release of AutoGMap:Learning to Map Large-scale Sparse Graphs on Memristive Crossbar For reproduction of our searched model, the Ubuntu OS is recommen

2 Aug 23, 2022
Vision-and-Language Navigation in Continuous Environments using Habitat

Vision-and-Language Navigation in Continuous Environments (VLN-CE) Project Website — VLN-CE Challenge — RxR-Habitat Challenge Official implementations

Jacob Krantz 132 Jan 02, 2023
Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.

human-pose-estimation-3d-python-cpp RealSenseD435 (RGB) 480x640 + CPU Corei9 45 FPS (Depth is not used) 1. Run 1-1. RealSenseD435 (RGB) 480x640 + CPU

Katsuya Hyodo 8 Oct 03, 2022
PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased

Kaihua Tang 824 Jan 03, 2023