ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence (NAACL 2022)

This repository contains the essential code for the paper ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence (NAACL 2022).

Prerequisites

The code is written by Python 3.6 in Linux system. The cuda version is 10.2. The necessary packages include:

torch==1.7.1 
transformers==4.17.0 
numpy==1.19.2 
scikit-learn==0.24.2
pandas==1.5.3
simpletransformers

ConfliBERT Checkpoints

We provided four versions of ConfliBERT:

1. ConfliBERT-scr-uncased:      Pretraining from scratch with our own uncased vocabulary (preferred)
2. ConfliBERT-scr-cased:      Pretraining from scratch with our own cased vocabulary
3. ConfliBERT-cont-uncased:      Continual pretraining with original BERT's uncased vocabulary
4. ConfliBERT-cont-cased:      Continual pretraining with original BERT's cased vocabulary

You can import the above four models directly via Huggingface API:

from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("snowood1/ConfliBERT-scr-uncased", use_auth_token=True)
model = AutoModelForMaskedLM.from_pretrained("snowood1/ConfliBERT-scr-uncased", use_auth_token=True)

Evaluation

The usage of ConfliBERT is the same as other BERT models in Huggingface.

We provided multiple examples using Simple Transformers. You can run:

CUDA_VISIBLE_DEVICES=0 python finetune_data.py --dataset IndiaPoliceEvents_sents --report_per_epoch

Click the Colab demo to see an example of evaluation:

Evaluation Datasets

Below is the summary of the publicly available datasets:

Dataset	Links
20Newsgroups	https://www.kaggle.com/crawford/20-newsgroups
BBCnews	https://www.kaggle.com/c/learn-ai-bbc/overview
EventStatusCorpus	https://catalog.ldc.upenn.edu/LDC2017T09
GlobalContention	https://github.com/emerging-welfare/glocongold/tree/master/sample
GlobalTerrorismDatabase	https://www.start.umd.edu/gtd/
Gun Violence Database	http://gun-violence.org/download/
IndiaPoliceEvents	https://github.com/slanglab/IndiaPoliceEvents
InsightCrime	https://figshare.com/s/73f02ab8423bb83048aa
MUC-4	https://github.com/xinyadu/grit_doc_event_entity/tree/master/data/muc
re3d	https://github.com/juand-r/entity-recognition-datasets/tree/master/data/re3d
SATP	https://github.com/javierosorio/SATP
CAMEO	https://dl.acm.org/doi/abs/10.1145/3514094.3534178

To use your own datasets, the 1st step is to preprocess the datasets into the required formats in ./data. For example,

1. IndiaPoliceEvents_sents for classfication tasks. The format is sentence + labels separated by tabs.
2. re3d for NER tasks in CONLL format

The 2nd step is to create the corresponding config files in ./configs with the correct tasks from ["binary", "multiclass", "multilabel", "ner"].

Pretraining Corpus

We have gathered a large corpus in politics and conflicts domain (33 GB) for pretraining ConfliBERT. The folder ./pretrain-corpora/Crawlers and Processes contains the sample scripts used to generate the corpus used in this study. Due to the copyright, we provide a few samples in ./pretrain-corpora/Samples. These samples follow the format of "one sentence per line format". See more details of pretraining corpora in our paper's Section 2 and Appendix.

Pretraining Scripts

We followed the same pretraining scripts run_mlm.py from Huggingface (The original link). Below is an example using 8 GPUs. We have provided our parameters in the Appendix. However, you should change the parameters according to your own devices:

	export NGPU=8; nohup python -m torch.distributed.launch --master_port 12345 \
	--nproc_per_node=$NGPU run_mlm.py \
	--model_type bert \
	--config_name ./bert_base_cased \
	--tokenizer_name ./bert_base_cased \
	--output_dir ./bert_base_cased \
	--cache_dir ./cache_cased_128 \
	--use_fast_tokenizer \
	--overwrite_output_dir \
	--train_file YOUR_TRAIN_FILE \
	--validation_file YOUR_VALID_FILE \
	--max_seq_length 128\ 
	--preprocessing_num_workers 4 \
	--dataloader_num_workers 2 \
	--do_train --do_eval \
	--learning_rate 5e-4 \
	--warmup_steps=10000 \
	--save_steps 1000 \
	--evaluation_strategy steps \
	--eval_steps 10000 \
	--prediction_loss_only  \
	--save_total_limit 3 \
	--per_device_train_batch_size 64 --per_device_eval_batch_size 64 \
	--gradient_accumulation_steps 4 \
	--logging_steps=100 \
	--max_steps 100000 \
	--adam_beta1 0.9 --adam_beta2 0.98 --adam_epsilon 1e-6 \
	--fp16 True --weight_decay=0.01

Citation

If you find this repo useful in your research, please consider citing:

@inproceedings{hu2022conflibert,
  title={ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence},
  author={Hu, Yibo and Hosseini, MohammadSaleh and Parolin, Erick Skorupa and Osorio, Javier and Khan, Latifur and Brandt, Patrick and D’Orazio, Vito},
  booktitle={Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={5469--5482},
  year={2022}
}