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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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

Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

Learning from Synthetic Shadows for Shadow Detection and Removal [Inoue+, IEEE TCSVT 2020].

Video Matting Refinement For Python

Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"

An NLP library with Awesome pre-trained Transformer models and easy-to-use interface, supporting wide-range of NLP tasks from research to industrial applications.

PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data.

This repository compare a selfie with images from identity documents and response if the selfie match.

PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"

PyTorch reimplementation of REALM and ORQA

This project implements "virtual speed" from heart rate monito

Neural Message Passing for Computer Vision

Convert onnx models to pytorch.

Computational Methods Course at UdeA. Forked and size reduced from:

ML course - EPFL Machine Learning Course, Fall 2021

Data, model training, and evaluation code for "PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models".

[NeurIPS-2021] Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

Решения, подсказки, тесты и утилиты для тренировки по алгоритмам от Яндекса.

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