Trex is a tool to match semantically similar functions based on transfer learning.

Related tags

Text Data & NLPtrex
Overview

Introduction

Trex is a tool to match semantically similar functions based on transfer learning.

Installation

We recommend conda to setup the environment and install the required packages.

First, create the conda environment,

conda create -n trex python=3.8 numpy scipy scikit-learn requests

and activate the conda environment:

conda activate trex

Then, install the latest PyTorch (assume you have GPU):

conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia

Enter the trex root directory: e.g., path/to/trex, and install trex:

pip install --editable .

For large datasets install PyArrow:

pip install pyarrow

For faster training install NVIDIA's apex library:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
  --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
  --global-option="--fast_multihead_attn" ./

Preparation

Pretrained models:

Create the checkpoints and checkpoints/pretrain subdirectory in path/to/trex

mkdir checkpoints, mkdir checkpoints/pretrain

Download our pretrained weight parameters and put in checkpoints/pretrain

Sample data for finetuning similarity

We provide the sample training/testing files of finetuning in data-src/similarity If you want to prepare the finetuning data yourself, make sure you follow the format shown in data-src/similarity (coming soon: tokenization script).

We have to binarize the data to make it ready to be trained. To binarize the training data for finetuning, run:

python command/finetune/preprocess.py

The binarized training data ready for finetuning (for detecting similarity) will be stored at data-bin/similarity

Training

To finetune the model, run:

./command/finetune/finetune.sh

The scripts loads the pretrained weight parameters from checkpoints/pretrain/ and finetunes the model.

Sample data for pretraining on micro-traces

We also provide (10K) samples and scripts to demonstrate how to pretrain the model. To binarize the training data for pretraining, run:

python command/pretrain/preprocess_pretrain_10k.py

The binarized training data ready for pretraining will be stored at data-bin/pretrain_10k

To pretrain the model, run:

./command/pretrain/pretrain_10k.sh

The pretrained model will be checkpointed at checkpoints/pretrain_10k

Dataset

We put our dataset here.

Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 models for speech recognition

Wav2Vec2 STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 mode

David Zurow 22 Dec 29, 2022
Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

9 Jan 08, 2023
Official PyTorch implementation of SegFormer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Figure 1: Performance of SegFormer-B0 to SegFormer-B5. Project page

NVIDIA Research Projects 1.4k Dec 29, 2022
Legal text retrieval for python

legal-text-retrieval Overview This system contains 2 steps: generate training data containing negative sample found by mixture score of cosine(tfidf)

Nguyễn Minh Phương 22 Dec 06, 2022
Sentiment Analysis Project using Count Vectorizer and TF-IDF Vectorizer

Sentiment Analysis Project This project contains two sentiment analysis programs for Hotel Reviews using a Hotel Reviews dataset from Datafiniti. The

Simran Farrukh 0 Mar 28, 2022
A simple Speech Emotion Recognition (SER) API created using Flask and running in a Docker container.

keyword_searching Steps to use this Python scripts: (1)Paste this script into the file folder containing the PDF files you need to search from; (2)Thi

2 Nov 11, 2022
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

Trankit: A Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing Trankit is a light-weight Transformer-based Pyth

652 Jan 06, 2023
Generate a cool README/About me page for your Github Profile

Github Profile README/ About Me Generator 💯 This webapp lets you build a cool README for your profile. A few inputs + ~15 mins = Your Github Profile

Rahul Banerjee 179 Jan 07, 2023
SIGIR'22 paper: Axiomatically Regularized Pre-training for Ad hoc Search

Introduction This codebase contains source-code of the Python-based implementation (ARES) of our SIGIR 2022 paper. Chen, Jia, et al. "Axiomatically Re

Jia Chen 17 Nov 09, 2022
Syntax-aware Multi-spans Generation for Reading Comprehension (TASLP 2022)

SyntaxGen Syntax-aware Multi-spans Generation for Reading Comprehension (TASLP 2022) In this repo, we upload all the scripts for this work. Due to siz

Zhuosheng Zhang 3 Jun 13, 2022
Rethinking the Truly Unsupervised Image-to-Image Translation - Official PyTorch Implementation (ICCV 2021)

Rethinking the Truly Unsupervised Image-to-Image Translation (ICCV 2021) Each image is generated with the source image in the left and the average sty

Clova AI Research 436 Dec 27, 2022
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields [project page][paper][cite] Geometry-Consistent Neural Shape Represe

Yifan Wang 100 Dec 19, 2022
this repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

1 Nov 02, 2021
Conversational text Analysis using various NLP techniques

Conversational text Analysis using various NLP techniques

Rita Anjana 159 Jan 06, 2023
Large-scale Knowledge Graph Construction with Prompting

Large-scale Knowledge Graph Construction with Prompting across tasks (predictive and generative), and modalities (language, image, vision + language, etc.)

ZJUNLP 161 Dec 28, 2022
Tool to add main subject to items on Wikidata using a WMFs CirrusSearch for named entity recognition or a manually supplied list of QIDs

ItemSubjector Tool made to add main subject statements to items based on the title using a home-brewed CirrusSearch-based Named Entity Recognition alg

Dennis Priskorn 9 Nov 17, 2022
DeBERTa: Decoding-enhanced BERT with Disentangled Attention

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 03, 2023
The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.

Neural Machine Translation communication system The model is basically direct to convert one source language to another targeted language using encode

Nishant Banjade 7 Sep 22, 2022
Simple Annotated implementation of GPT-NeoX in PyTorch

Simple Annotated implementation of GPT-NeoX in PyTorch This is a simpler implementation of GPT-NeoX in PyTorch. We have taken out several optimization

labml.ai 101 Dec 03, 2022
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