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 the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Code repository for EMNLP 2021 paper 'Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods'

PushForKiCad - AISLER Push for KiCad EDA

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DSL for matching Python ASTs

Convenient tool for speeding up the intern/officer review process.

TakeInfoatNistforICS - Take Information in NIST NVD for ICS

Instance-wise Feature Importance in Time (FIT)

Official Repository for the ICCV 2021 paper "PixelSynth: Generating a 3D-Consistent Experience from a Single Image"

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

Framework web SnakeServer.

Code for DeepCurrents: Learning Implicit Representations of Shapes with Boundaries

Multi-Joint dynamics with Contact. A general purpose physics simulator.

[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

Fast and robust clustering of point clouds generated with a Velodyne sensor.

SpecAugmentPyTorch - A Pytorch (support batch and channel) implementation of GoogleBrain's SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Binary classification for arrythmia detection with ECG datasets.

Pytorch implemenation of Stochastic Multi-Label Image-to-image Translation (SMIT)

Conceptual 12M is a dataset containing (image-URL, caption) pairs collected for vision-and-language pre-training.

A script that trains a model to recognize handwritten digits using the MNIST data set.

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 the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Code repository for EMNLP 2021 paper 'Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods'

PushForKiCad - AISLER Push for KiCad EDA

Measuring Coding Challenge Competence With APPS

DSL for matching Python ASTs

Convenient tool for speeding up the intern/officer review process.

TakeInfoatNistforICS - Take Information in NIST NVD for ICS

Instance-wise Feature Importance in Time (FIT)

Official Repository for the ICCV 2021 paper "PixelSynth: Generating a 3D-Consistent Experience from a Single Image"

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

Framework web SnakeServer.

Code for DeepCurrents: Learning Implicit Representations of Shapes with Boundaries

Multi-Joint dynamics with Contact. A general purpose physics simulator.

[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

Fast and robust clustering of point clouds generated with a Velodyne sensor.

SpecAugmentPyTorch - A Pytorch (support batch and channel) implementation of GoogleBrain's SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Binary classification for arrythmia detection with ECG datasets.

Pytorch implemenation of Stochastic Multi-Label Image-to-image Translation (SMIT)

Conceptual 12M is a dataset containing (image-URL, caption) pairs collected for vision-and-language pre-training.

A script that trains a model to recognize handwritten digits using the MNIST data set.

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