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contacted cancel submit feedback saved searches use saved searches to filter your results more quickly name query to see all available qualifiers see our documentation cancel create saved search sign in sign up appearance settings resetting focus you signed in with another tab or window reload to refresh your session you signed out in another tab or window reload to refresh your session you switched accounts on another tab or window reload to refresh your session dismiss alert message lxucs coref hoi public notifications you must be signed in to change notification settings fork 19 star 60 code issues 6 pull requests 0 actions projects security and quality 0 insights additional navigation options code issues pull requests actions projects security and quality insights lxucs coref hoi master branches tags go to file code open more actions menu folders and files name name last commit message last commit date latest commit history 11 commits 11 commits gitignore gitignore license license readme md readme md analyze py analyze py conll py conll py evaluate py evaluate py experiments conf experiments conf higher_order py higher_order py metrics py metrics py model py model py predict py predict py preprocess py preprocess py requirements txt requirements txt run py run py setup_data sh setup_data sh tensorize py tensorize py util py util py view all files repository files navigation readme apache 2 0 license more items end to end coreference resolution with different higher order inference methods this repository contains the implementation of the paper revealing the myth of higher order inference in coreference resolution architecture the basic end to end coreference model is a pytorch re implementation based on the tensorflow model following similar preprocessing see this repository there are four higher order inference hoi methods experimented attended antecedent entity equalization span clustering and cluster merging all are included here except for entity equalization which is experimented in the equivalent tensorflow environment see this separate repository files run py training and evaluation model py the coreference model higher_order py higher order inference modules predict py script for prediction on custom input analyze py result analysis preprocess py converting conll files to examples tensorize py tensorizing example conll py metrics py same conll related files from the repository experiments conf different model configurations basic setup set up environment and data for training and evaluation install python3 dependencies pip install r requirements txt create a directory for data that will contain all data files models and log files set data_dir path to data dir in experiments conf prepare dataset requiring ontonotes 5 0 corpus setup_data sh path to ontonotes path to data dir for spanbert download the pretrained weights from this repository and rename it path to data dir spanbert_base or path to data dir spanbert_large accordingly evaluation provided trained models spanbert no hoi file spanbert attended antecedent file spanbert span clustering file spanbert cluster merging file spanbert entity equalization see repository the name of each directory corresponds with a configuration in experiments conf each directory has two trained models inside if you want to use the official evaluator download and unzip conll 2012 scorer under this directory evaluate a model on the dev test set download the corresponding model directory and unzip it under data_dir python evaluate py config model_id gpu_id e g attended antecedent python evaluate py train_spanbert_large_ml0_d2 may08_12 38 29_58000 0 prediction prediction on custom input see python predict py h interactive user input python predict py config_name config model_identifier model_id gpu_id gpu_id e g python predict py config_name train_spanbert_large_ml0_d1 model_identifier may10_03 28 49_54000 gpu_id 0 input from file jsonlines file of this format python predict py config_name config model_identifier model_id gpu_id gpu_id jsonlines_path input_path output_path output_path training python run py config gpu_id config can be any configuration in experiments conf log file will be saved at your_data_dir config log_xxx txt models will be saved at your_data_dir config model_xxx bin tensorboard is available at your_data_dir tensorboard configurations some important configurations in experiments conf data_dir the full path to the directory containing dataset models log files coref_depth and higher_order controlling the higher order inference module bert_pretrained_name_or_path the name path of the pretrained bert model huggingface bert models max_training_sentences the maximum segments to use when document is too long for bert large and spanbert large set to 3 for 32gb gpu or 2 for 24gb gpu citation inproceedings xu choi 2020 revealing title revealing the myth of higher order inference in coreference resolution author xu liyan and choi jinho d booktitle proceedings of the 2020 conference on empirical methods in natural language processing emnlp month nov year 2020 publisher association for computational linguistics url https www aclweb org anthology 2020 emnlp main 686 pages 8527 8533 about pytorch implementation of the end to end coreference resolution model with different higher order inference methods topics nlp pytorch higher order coreference resolution resources readme license apache 2 0 license uh oh there was an error while loading please reload this page activity stars 60 stars watchers 1 watching forks 19 forks report repository releases no releases published packages 0 uh oh there was an error while loading please reload this page contributors uh oh there was an error while loading please reload this page languages python 98 8 shell 1 2 footer 2026 github inc footer navigation terms privacy security status community docs contact manage cookies do not share my personal information you can t perform that action at 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