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

Notebook and code to synthesize complex and highly dimensional datasets using Gretel APIs.

A Kaggle competition: discriminate gender based on handwriting

Neural models of common sense. 🤖

A collection of resources and papers on Diffusion Models, a darkhorse in the field of Generative Models

A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.

OpenGAN: Open-Set Recognition via Open Data Generation

Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

Self-supervised learning on Graph Representation Learning (node-level task)

ML-based medical imaging using Azure

Predict bus arrival time using VertexAI and Nvidia's Jetson Nano

PyTorch code for the paper "Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval".

quantize aware training package for NCNN on pytorch

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

This is a simple framework to make object detection dataset very quickly

A general framework for deep learning experiments under PyTorch based on pytorch-lightning

Repository of Vision Transformer with Deformable Attention

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