This code implements constituency parse tree aggregation

Related tags

Deep LearningCPTAM
Overview

README

This code implements constituency parse tree aggregation.

Folder details

  • code: This folder contains the code that implements constituency parse tree aggregation.
  • sample_dataset: This folder contains 100 sentences from Penn Treebank dataset. This is the input for the method. Ground Truth is only used for evaluation purposes.

Code description

  • hanlp_resources.py: The output of Hanlp parser follows a different format. This code is used to convert it into the format of other parsers.
  • resources.py: This code does character indexing of the input, obtains cluster list and stores the formatted input into a dictionary.
  • compatibility.py: This code contains implementation of maximum independent set.
  • medcpt.py: This code does constituency parse tree aggregation.
  • evaluation.py: This code does performance evaluation and stores the results as a dictionary file.
  • print_results.py: This code prints evaluation results.

Input parsers

Dataset details

Baseline Aggregation methods

The implementation of baseline aggregation methods can be found at https://evolution.genetics.washington.edu/phylip/getme-new1.html

Steps for code execution

Required packages

  • python 3
  • pickle
  • numpy

Execution flow

  • python resources.py
  • python medcpt.py
  • python evaluation.py
  • python print_results.py
Owner
Adithya Kulkarni
Adithya Kulkarni
PyTorch implementation of "Optimization Planning for 3D ConvNets"

Optimization-Planning-for-3D-ConvNets Code for the ICML 2021 paper: Optimization Planning for 3D ConvNets. Authors: Zhaofan Qiu, Ting Yao, Chong-Wah N

Zhaofan Qiu 2 Jan 12, 2022
Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt. This is done by

Mehdi Cherti 135 Dec 30, 2022
Official implementation of "Refiner: Refining Self-attention for Vision Transformers".

RefinerViT This repo is the official implementation of "Refiner: Refining Self-attention for Vision Transformers". The repo is build on top of timm an

101 Dec 29, 2022
GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

MTV-TSA: Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions. This is the official code release fo

owl 37 Dec 24, 2022
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds This repository contains the code asscoiated

Felix Hensel 14 Dec 12, 2022
Random-Afg - Afghanistan Random Old Idz Cloner Tools

AFGHANISTAN RANDOM OLD IDZ CLONER TOOLS Install $ apt update $ apt upgrade $ apt

MAHADI HASAN AFRIDI 5 Jan 26, 2022
A symbolic-model-guided fuzzer for TLS

tlspuffin TLS Protocol Under FuzzINg A symbolic-model-guided fuzzer for TLS Master Thesis | Thesis Presentation | Documentation Disclaimer: The term "

69 Dec 20, 2022
Code for our ALiBi method for transformer language models.

Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation This repository contains the code and models for our paper Tra

Ofir Press 211 Dec 31, 2022
Implementation of self-attention mechanisms for general purpose. Focused on computer vision modules. Ongoing repository.

Self-attention building blocks for computer vision applications in PyTorch Implementation of self attention mechanisms for computer vision in PyTorch

AI Summer 962 Dec 23, 2022
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling For Official repo of NU-Wave: A Diffusion Probabilistic Model for Neural Audio Up

Rishikesh (ऋषिकेश) 38 Oct 11, 2022
Neural network-based build time estimation for additive manufacturing

Neural network-based build time estimation for additive manufacturing Oh, Y., Sharp, M., Sprock, T., & Kwon, S. (2021). Neural network-based build tim

Yosep 1 Nov 15, 2021
Learning to Prompt for Continual Learning

Learning to Prompt for Continual Learning (L2P) Official Jax Implementation L2P is a novel continual learning technique which learns to dynamically pr

Google Research 207 Jan 06, 2023
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions

gtfs2vec This is a companion repository for a gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions publication. Vis

Politechnika Wrocławska - repozytorium dla informatyków 5 Oct 10, 2022
This is code of book "Learn Deep Learning with PyTorch"

深度学习入门之PyTorch Learn Deep Learning with PyTorch 非常感谢您能够购买此书,这个github repository包含有深度学习入门之PyTorch的实例代码。由于本人水平有限,在写此书的时候参考了一些网上的资料,在这里对他们表示敬意。由于深度学习的技术在

Xingyu Liao 2.5k Jan 04, 2023
CPU inference engine that delivers unprecedented performance for sparse models

The DeepSparse Engine is a CPU runtime that delivers unprecedented performance by taking advantage of natural sparsity within neural networks to reduce compute required as well as accelerate memory b

Neural Magic 1.2k Jan 09, 2023
This repository contains datasets and baselines for benchmarking Chinese text recognition.

Benchmarking-Chinese-Text-Recognition This repository contains datasets and baselines for benchmarking Chinese text recognition. Please see the corres

FudanVI Lab 254 Dec 30, 2022
Model parallel transformers in Jax and Haiku

Mesh Transformer Jax A haiku library using the new(ly documented) xmap operator in Jax for model parallelism of transformers. See enwik8_example.py fo

Ben Wang 4.8k Jan 01, 2023
🙄 Difficult algorithm, Simple code.

🎉TensorFlow2.0-Examples🎉! "Talk is cheap, show me the code." ----- Linus Torvalds Created by YunYang1994 This tutorial was designed for easily divin

1.7k Dec 25, 2022
The easiest tool for extracting radiomics features and training ML models on them.

Simple pipeline for experimenting with radiomics features Installation git clone https://github.com/piotrekwoznicki/ClassyRadiomics.git cd classrad pi

Piotr Woźnicki 17 Aug 04, 2022
Our implementation used for the MICCAI 2021 FLARE Challenge titled 'Efficient Multi-Organ Segmentation Using SpatialConfiguartion-Net with Low GPU Memory Requirements'.

Efficient Multi-Organ Segmentation Using SpatialConfiguartion-Net with Low GPU Memory Requirements Our implementation used for the MICCAI 2021 FLARE C

Franz Thaler 3 Sep 27, 2022