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

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Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation in PyTorch

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Rule based classification A hotel s customers dataset

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A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

Self-supervised spatio-spectro-temporal represenation learning for EEG analysis

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Full Stack Deep Learning Labs

The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

PyTorch implementation of the implicit Q-learning algorithm (IQL)

Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery"

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

Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation (ICCV 2021)

[NeurIPS 2021] "Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks" by Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, Yingyan Lin

TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers.

Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation in PyTorch

Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression

Pytorch code for our paper "Feedback Network for Image Super-Resolution" (CVPR2019)

A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset

😮The official implementation of "CoNeRF: Controllable Neural Radiance Fields" 😮

Rule based classification A hotel s customers dataset

A simple API wrapper for Discord interactions.

A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

Self-supervised spatio-spectro-temporal represenation learning for EEG analysis

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

Full Stack Deep Learning Labs

The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

PyTorch implementation of the implicit Q-learning algorithm (IQL)

Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery"

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