Requirements

• Python (tested on 3.9.7)
• CUDA (tested on 11.4)
• PyTorch (tested on 1.10.1)
• Transformers (tested on 4.15.0)
• numpy (tested on 1.20.3)
• wandb
• ujson
• tqdm

Dataset

The DocRED dataset can be downloaded following the instructions at link.

Code
 |-- docred
 |    |-- train_annotated.json        
 |    |-- train_distant.json
 |    |-- dev.json
 |    |-- test.json
 |    |-- rel2id.json
 |    |-- ner2id.json
 |    |-- rel_info.json
 |-- logs
 |-- result
 |-- model
 |    |-- bert        
 |    |-- save_model

Notice:

1. The code of our GTN is located in the Graph_Encoder.py file.
2. The code of the Discriminator in our SA-KD is located in the class Discriminator() of model.py.

Training and Evaluation on DocRED

Training Model

Train the BERT-based model on DocRED with the following command:

>> sh run_bert.sh 

Hyper-parameters Setting

#! /bin/bash
export CUDA_VISIBLE_DEVICES=1
python3 -u train.py \
  --data_dir ./docred \
  --transformer_type bert \
  --model_name_or_path ./model/bert \
  --save_path ./model/save_model/ \
  --load_path ./model/save_model/ \
  --train_file train_annotated.json \
  -- train_base \
  --dev_file dev.json \
  --test_file test.json \
  --train_batch_size 16 \
  --test_batch_size 16 \
  --gradient_accumulation_steps 1 \
  --num_labels 4 \
  --decoder_layers 1 \
  --learning_rate 1e-4 \
  --encoder_lr 2e-5 \
  --max_grad_norm 1.0 \
  --warmup_ratio 0.06 \
  --num_train_epochs 52 \
  --seed 66 \
  --num_class 97 \
  | tee logs/logs.train.log 2>&1

The trained base module is saved in ./model/save_model/.

Evaluating Model

First, you can set the model to be loaded on line 381 of train.py.

Then, Test our entire model on DocRED with the following command:

>> sh run_bert.sh 

Hyper-parameters Setting

#! /bin/bash
export CUDA_VISIBLE_DEVICES=1
python3 -u train.py \
  --data_dir ./docred \
  --transformer_type bert \
  --model_name_or_path ./model/bert \
  --save_path ./model/save_model/ \
  --load_path ./model/save_model/ \
  --train_file train_annotated.json \
  --test \
  --dev_file dev.json \
  --test_file test.json \
  --train_batch_size 9 \
  --test_batch_size 16 \
  --gradient_accumulation_steps 1 \
  --num_labels 4 \
  --decoder_layers 1 \
  --learning_rate 1e-4 \
  --encoder_lr 1e-5 \
  --max_grad_norm 1.0 \
  --warmup_ratio 0.06 \
  --num_train_epochs 52 \
  --seed 66 \
  --num_class 97 \
  | tee logs/logs.train.log 2>&1

The program will generate a test file ./result/result.json in the official evaluation format. You can compress and submit it to Colab for the official test score.