For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

Last update: Nov 02, 2022

Related tags

Deep Learning SciBERTSUM

Overview

LongScientificFormer

For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

Some codes are borrowed from ONMT(https://github.com/OpenNMT/OpenNMT-py)

Data Preparation

Option 1: download the processed data

Pre-processed data

Put all files into raw_data directory

Step 2. Download Stanford CoreNLP

We will need Stanford CoreNLP to tokenize the data. Download it here and unzip it. Then add the following command to your bash_profile:

export CLASSPATH=/path/to/stanford-corenlp-4.2.2/stanford-corenlp-4.2.2.jar

replacing /path/to/ with the path to where you saved the stanford-corenlp-4.2.2 directory.

step 3. extracting sections from GROBID XML files

python preprocess.py -mode extract_pdf_sections -log_file ../logs/extract_section.log

step 4. extracting text from TIKA XML files

python preprocess.py -mode get_text_clean_tika -log_file ../logs/extract_tika_text.log

step 5. Tokenize texts from papers and slides using stanfordCoreNLP

python preprocess.py -mode tokenize  -save_path ../temp -log_file ../logs/tokenize_by_corenlp.log

Step 6. Extract source, section, and target from tokenized files

python preprocess.py -mode clean_paper_jsons -save_path ../json_data/  -n_cpus 10 -log_file ../logs/build_json.log

Step 7. Generate BERT `.pt` files from source, sections and targets

python preprocess.py -mode format_to_bert -raw_path ../json_data/ -save_path ../bert_data  -lower -n_cpus 40 -log_file ../logs/build_bert_files.log

Model Training

First run: For the first time, you should use single-GPU, so the code can download the BERT model. Use -visible_gpus -1, after downloading, you could kill the process and rerun the code with multi-GPUs.

Train

python train.py  -ext_dropout 0.1 -lr 2e-3  -visible_gpus 1,2,3 -report_every 200 -save_checkpoint_steps 1000 -batch_size 1 -train_steps 100000 -accum_count 2  -log_file ../logs/ext_bert -use_interval true -warmup_steps 10000

To continue training from a checkpoint

python train.py  -ext_dropout 0.1 -lr 2e-3  -train_from ../models/model_step_99000.pt -visible_gpus 1,2,3 -report_every 200 -save_checkpoint_steps 1000 -batch_size 1 -train_steps 100000 -accum_count 2  -log_file ../logs/ext_bert -use_interval true -warmup_steps 10000

Test

python train.py -mode test  -test_batch_size 1 -bert_data_path ../bert_data -log_file ../logs/ext_bert_test -test_from ../models/model_step_99000.pt -model_path ../models -sep_optim true -use_interval true -visible_gpus 1,2,3 -alpha 0.95 -result_path ../results/ext

For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

Related tags

Overview

LongScientificFormer

Data Preparation

Option 1: download the processed data

Step 2. Download Stanford CoreNLP

step 3. extracting sections from GROBID XML files

step 4. extracting text from TIKA XML files

step 5. Tokenize texts from papers and slides using stanfordCoreNLP

Step 6. Extract source, section, and target from tokenized files

Step 7. Generate BERT `.pt` files from source, sections and targets

Model Training

Train

Test

Owner

Athar Sefid

Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022

An example showing how to use jax to train resnet50 on multi-node multi-GPU

Build a medical knowledge graph based on Unified Language Medical System (UMLS)

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

MegEngine implementation of YOLOX

SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.

A Pythonic library for Nvidia Codec.

THIS IS THE OLD PYMC PROJECT. PLEASE USE PYMC3 INSTEAD:

This is the official PyTorch implementation of the CVPR 2020 paper "TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting".

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach

This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

Chainer implementation of recent GAN variants

BigbrotherBENL - Face recognition on the Big Brother episodes in Belgium and the Netherlands.

pytorch implementation for PointNet

Using LSTM to detect spoofing attacks in an Air-Ground network

Clustergram - Visualization and diagnostics for cluster analysis in Python

Repository for the paper "Online Domain Adaptation for Occupancy Mapping", RSS 2020

RetinaNet-PyTorch - A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection

Real life contra a deep learning project built using mediapipe and openc

For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

Related tags

Overview

LongScientificFormer

Data Preparation

Option 1: download the processed data

Step 2. Download Stanford CoreNLP

step 3. extracting sections from GROBID XML files

step 4. extracting text from TIKA XML files

step 5. Tokenize texts from papers and slides using stanfordCoreNLP

Step 6. Extract source, section, and target from tokenized files

Step 7. Generate BERT .pt files from source, sections and targets

Model Training

Train

Test

Owner

Athar Sefid

Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022

An example showing how to use jax to train resnet50 on multi-node multi-GPU

Build a medical knowledge graph based on Unified Language Medical System (UMLS)

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

MegEngine implementation of YOLOX

SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.

A Pythonic library for Nvidia Codec.

THIS IS THE **OLD** PYMC PROJECT. PLEASE USE PYMC3 INSTEAD:

This is the official PyTorch implementation of the CVPR 2020 paper "TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting".

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach

This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

Chainer implementation of recent GAN variants

BigbrotherBENL - Face recognition on the Big Brother episodes in Belgium and the Netherlands.

pytorch implementation for PointNet

Using LSTM to detect spoofing attacks in an Air-Ground network

Clustergram - Visualization and diagnostics for cluster analysis in Python

Repository for the paper "Online Domain Adaptation for Occupancy Mapping", RSS 2020

RetinaNet-PyTorch - A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection

Real life contra a deep learning project built using mediapipe and openc

Step 7. Generate BERT `.pt` files from source, sections and targets

THIS IS THE OLD PYMC PROJECT. PLEASE USE PYMC3 INSTEAD: