pip install -r requirements.txt

bash data/download.sh supervised

Between	O
1789	O
and	O
1793	O
he	O
sat	O
on	O
a	O
committee	O
reviewing	O
the	O
administrative	MISC-law
constitution	MISC-law
of	MISC-law
Galicia	MISC-law
to	O
little	O
effect	O
.	O

--util
| -- framework.py
| -- data_loader.py
| -- viterbi.py             # viterbi decoder for structshot only
| -- word_encoder
| -- fewshotsampler.py

-- proto.py                 # prototypical model
-- nnshot.py                # nnshot model

-- train_demo.py            # main training script

-- mode                 training mode, must be inter, intra, or supervised
-- trainN               N in train
-- N                    N in val and test
-- K                    K shot
-- Q                    Num of query per class
-- batch_size           batch size
-- train_iter           num of iters in training
-- val_iter             num of iters in validation
-- test_iter            num of iters in testing
-- val_step             val after training how many iters
-- model                model name, must be proto, nnshot or structshot
-- max_length           max length of tokenized sentence
-- lr                   learning rate
-- weight_decay         weight decay
-- grad_iter            accumulate gradient every x iterations
-- load_ckpt            path to load model
-- save_ckpt            path to save model
-- fp16                 use nvidia apex fp16
-- only_test            no training process, only test
-- ckpt_name            checkpoint name
-- seed                 random seed
-- pretrain_ckpt        bert pre-trained checkpoint
-- dot                  use dot instead of L2 distance in distance calculation
-- use_sgd_for_bert     use SGD instead of AdamW for BERT.
# only for structshot
-- tau                  StructShot parameter to re-normalizes the transition probabilities

python3 train_demo.py  --train data/mydata/train-inter.txt \
--val data/mydata/val-inter.txt --test data/mydata/test-inter.txt \
--lr 1e-3 --batch_size 2 --trainN 5 --N 5 --K 1 --Q 1 \
--train_iter 10000 --val_iter 500 --test_iter 5000 --val_step 1000 \
--max_length 60 --model structshot --tau 0.32

python3 train_demo.py  --train data/mydata/train-inter.txt \
--val data/mydata/val-inter.txt --test data/mydata/test-inter.txt \
--lr 1e-3 --batch_size 2 --trainN 5 --N 5 --K 5 --Q 5 \
--train_iter 10000 --val_iter 500 --test_iter 5000 --val_step 1000 \
--max_length 60 --model structshot --tau 0.318

python3 train_demo.py  --train data/mydata/train-inter.txt \
--val data/mydata/val-inter.txt --test data/mydata/test-inter.txt \
--lr 1e-3 --batch_size 2 --trainN 10 --N 10 --K 1 --Q 1 \
--train_iter 10000 --val_iter 500 --test_iter 5000 --val_step 1000 \
--max_length 60 --model structshot --tau 0.32

python3 train_demo.py  --train data/mydata/train-inter.txt \
--val data/mydata/val-inter.txt --test data/mydata/test-inter.txt \
--lr 1e-3 --batch_size 2 --trainN 5 --N 5 --K 5 --Q 1 \
--train_iter 10000 --val_iter 500 --test_iter 5000 --val_step 1000 \
--max_length 60 --model structshot --tau 0.434

@inproceedings{ding2021few,
title={Few-NERD: A Few-Shot Named Entity Recognition Dataset},
author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan},
booktitle={ACL-IJCNLP},
year={2021}
}