Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Overview

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Task

Training huge unsupervised deep neural networks yields to strong progress in the field of Natural Language Processing (NLP). Using these extensively pre-trained networks for particular NLP applications is the current state-of-the-art approach. In this project, we approach the task of ranking possible clarifying questions for a given query. We fine-tuned a pre-trained BERT model to rank the possible clarifying questions in a classification manner. The achieved model scores a top-5 accuracy of 0.4565 on the provided benchmark dataset.

Installation

This project was originally developed with Python 3.8, PyTorch 1.7, and CUDA 11.0. The training requires one NVIDIA GeForce RTX 1080 (11GB memory).

  • Create conda environment:
conda create --name dl4nlp
source activate dl4nlp
  • Install the dependencies:
pip install -r requirements.txt

Run

We use a pretrained BERT-Base by Hugging Face and fine-tune it on the given training dataset. To run training, please use the following command:

python main.py --train

For evaluation on the test set, please use the following command:

python main.py --test

Arguments for training and/or testing:

  • --train: Run training on training dataset. Default: True
  • --val: Run evaluation during training on validation dataset. Default: True
  • --test: Run evaluation on test dataset. Default: True
  • --cuda-devices: Set GPU index Default: 0
  • --cpu: Run everything on CPU. Default: False
  • --data-parallel: Use DataParallel. Default: False
  • --data-root: Path to dataset folder. Default: data
  • --train-file-name: Name of training file name in data-root. Default: training.tsv
  • --test-file-name: Name of test file name in data-root. Default: test_set.tsv
  • --question-bank-name: Name of question bank file name in data-root. Default: question_bank.tsv
  • --checkpoints-root: Path to checkpoints folder. Default: checkpoints
  • --checkpoint-name: File name of checkpoint in checkpoints-root to start training or use for testing. Default: None
  • --runs-root: Path to output runs folder for tensorboard. Default: runs
  • --txt-root: Path to output txt folder for evaluation results. Default: txt
  • --lr: Learning rate. Default: 1e-5
  • --betas: Betas for optimization. Default: (0.9, 0.999)
  • --weight-decay: Weight decay. Default: 1e-2
  • --val-start: Set at which epoch to start validation. Default: 0
  • --val-step: Set at which epoch rate to valide. Default: 1
  • --val-split: Use subset of training dataset for validation. Default: 0.005
  • --num-epochs: Number of epochs for training. Default: 10
  • --batch-size: Samples per batch. Default: 32
  • --num-workers: Number of workers. Default: 4
  • --top-k-accuracy: Evaluation metric with flexible top-k-accuracy. Default: 50
  • --true-label: True label in dataset. Default: 1
  • --false-label: False label in dataset. Default: 0

Example output

User query:

Tell me about Computers

Propagated clarifying questions:

  1. do you like using computers
  2. do you want to know how to do computer programming
  3. do you want to see some closeup of a turbine
  4. are you looking for information on different computer programming languages
  5. are you referring to a software
Owner
Oliver Hahn
Master Thesis @ Visual Inference Lab | Grad Student @ Technical University of Darmstadt
Oliver Hahn
Face Depixelizer based on "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models" repository.

NOTE We have noticed a lot of concern that PULSE will be used to identify individuals whose faces have been blurred out. We want to emphasize that thi

Denis Malimonov 2k Dec 29, 2022
Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral)

Joint Discriminative and Generative Learning for Person Re-identification [Project] [Paper] [YouTube] [Bilibili] [Poster] [Supp] Joint Discriminative

NVIDIA Research Projects 1.2k Dec 30, 2022
Reference implementation for Structured Prediction with Deep Value Networks

Deep Value Network (DVN) This code is a python reference implementation of DVNs introduced in Deep Value Networks Learn to Evaluate and Iteratively Re

Michael Gygli 55 Feb 02, 2022
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation This reposi

First Person Vision @ Image Processing Laboratory - University of Catania 1 Aug 21, 2022
Mahadi-Now - This Is Pakistani Just Now Login Tools

PAKISTANI JUST NOW LOGIN TOOLS Install apt update apt upgrade apt install python

MAHADI HASAN AFRIDI 19 Apr 06, 2022
Simple, but essential Bayesian optimization package

BayesO: A Bayesian optimization framework in Python Simple, but essential Bayesian optimization package. http://bayeso.org Online documentation Instal

Jungtaek Kim 74 Dec 05, 2022
A generalized framework for prototyping full-stack cooperative driving automation applications under CARLA+SUMO.

OpenCDA OpenCDA is a SIMULATION tool integrated with a prototype cooperative driving automation (CDA; see SAE J3216) pipeline as well as regular autom

UCLA Mobility Lab 726 Dec 29, 2022
Defending graph neural networks against adversarial attacks (NeurIPS 2020)

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Authors: Xiang Zhang ( Zitnik Lab @ Harvard 44 Dec 07, 2022

This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Integrated Gradients This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found h

Tianhong Dai 150 Dec 23, 2022
Integrated physics-based and ligand-based modeling.

ComBind ComBind integrates data-driven modeling and physics-based docking for improved binding pose prediction and binding affinity prediction. Given

Dror Lab 44 Oct 26, 2022
SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts

[arXiv] The main motivation of the SHIFT15M project is to provide a dataset that contains natural dataset shifts collected from a web service IQON, wh

ZOZO, Inc. 138 Nov 24, 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations

HIVE: Evaluating the Human Interpretability of Visual Explanations Project Page | Paper This repo provides the code for HIVE, a human evaluation frame

Princeton Visual AI Lab 16 Dec 13, 2022
Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021

AutoInt: Automatic Integration for Fast Neural Volume Rendering CVPR 2021 Project Page | Video | Paper PyTorch implementation of automatic integration

Stanford Computational Imaging Lab 149 Dec 22, 2022
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators This is our Pytorch implementation for t

RUCAIBox 12 Jul 22, 2022
COIN the currently largest dataset for comprehensive instruction video analysis.

COIN Dataset COIN is the currently largest dataset for comprehensive instruction video analysis. It contains 11,827 videos of 180 different tasks (i.e

86 Dec 28, 2022
MoCoGAN: Decomposing Motion and Content for Video Generation

MoCoGAN: Decomposing Motion and Content for Video Generation This repository contains an implementation and further details of MoCoGAN: Decomposing Mo

Sergey Tulyakov 514 Dec 18, 2022
DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations

DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations This repository contains the data, scripts and baseline co

Alexa 51 Dec 17, 2022
Colar: Effective and Efficient Online Action Detection by Consulting Exemplars, CVPR 2022.

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars This repository is the official implementation of Colar. In this work,

LeYang 246 Dec 13, 2022
Multiple custom object count and detection using YOLOv3-Tiny method

Electronic-Component-YOLOv3 Introduce This project created to detect, count, and recognize multiple custom object using YOLOv3-Tiny method. The target

Derwin Mahardika 2 Nov 14, 2022
Official implementation for "Image Quality Assessment using Contrastive Learning"

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

Pavan Chennagiri 67 Dec 30, 2022