Spaces:
Build error
Build error
File size: 8,744 Bytes
6c25ddb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
from json import load
import os
import json
import re
from collections import defaultdict
import argparse
import transformers
from transformers import BartTokenizer
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from .data import IEDataset, my_collate
from .utils import load_ontology
MAX_LENGTH=170
MAX_TGT_LENGTH=72
class ACEDataModule(pl.LightningDataModule):
def __init__(self, args):
super().__init__()
self.hparams = args
self.tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
self.tokenizer.add_tokens([' <arg>',' <tgr>'])
def create_gold_gen(self, ex, ontology_dict,mark_trigger=True, index=0):
'''
If there are multiple events per example, use index parameter.
Input: <s> Template with special <arg> placeholders </s> </s> Passage </s>
Output: <s> Template with arguments and <arg> when no argument is found.
'''
evt_type = ex['event_mentions'][index]['event_type']
context_words = ex['tokens']
template = ontology_dict[evt_type]['template']
input_template = re.sub(r'<arg\d>', '<arg>', template)
space_tokenized_input_template = input_template.split()
tokenized_input_template = []
for w in space_tokenized_input_template:
tokenized_input_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
role2arg = defaultdict(list)
for argument in ex['event_mentions'][index]['arguments']:
role2arg[argument['role']].append(argument)
role2arg = dict(role2arg)
arg_idx2text = defaultdict(list)
for role in role2arg.keys():
if role not in ontology_dict[evt_type]:
# annotation error
continue
for i, argument in enumerate(role2arg[role]):
arg_text = argument['text']
if i < len(ontology_dict[evt_type][role]):
# enough slots to fill in
arg_idx = ontology_dict[evt_type][role][i]
else:
# multiple participants for the same role
arg_idx = ontology_dict[evt_type][role][-1]
arg_idx2text[arg_idx].append(arg_text)
for arg_idx, text_list in arg_idx2text.items():
text = ' and '.join(text_list)
template = re.sub('<{}>'.format(arg_idx), text, template)
trigger = ex['event_mentions'][index]['trigger']
# trigger span does not include last index
if mark_trigger:
prefix = self.tokenizer.tokenize(' '.join(context_words[:trigger['start']]), add_prefix_space=True)
tgt = self.tokenizer.tokenize(' '.join(context_words[trigger['start']: trigger['end']]), add_prefix_space=True)
suffix = self.tokenizer.tokenize(' '.join(context_words[trigger['end']:]), add_prefix_space=True)
context = prefix + [' <tgr>', ] + tgt + [' <tgr>', ] + suffix
else:
context = self.tokenizer.tokenize(' '.join(context_words), add_prefix_space=True)
output_template = re.sub(r'<arg\d>','<arg>', template )
space_tokenized_template = output_template.split()
tokenized_template = []
for w in space_tokenized_template:
tokenized_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
return tokenized_input_template, tokenized_template, context
def prepare_data(self):
if self.hparams.tmp_dir:
data_dir = self.hparams.tmp_dir
else:
data_dir = 'preprocessed_{}'.format(self.hparams.dataset)
if not os.path.exists(data_dir):
print('creating tmp dir ....')
os.makedirs(data_dir)
if self.hparams.dataset == 'combined':
ontology_dict = load_ontology(dataset='KAIROS')
else:
ontology_dict = load_ontology(dataset=self.hparams.dataset)
for split,f in [('train',self.hparams.train_file), ('val',self.hparams.val_file), ('test',self.hparams.test_file)]:
if (split in ['train', 'val']) and not f: #possible for eval_only
continue
with open(f,'r') as reader, open(os.path.join(data_dir,'{}.jsonl'.format(split)), 'w') as writer:
for lidx, line in enumerate(reader):
ex = json.loads(line.strip())
for i in range(len(ex['event_mentions'])):
evt_type = ex['event_mentions'][i]['event_type']
if evt_type not in ontology_dict: # should be a rare event type
print(evt_type)
continue
input_template, output_template, context= self.create_gold_gen(ex, ontology_dict, self.hparams.mark_trigger, index=i)
input_tokens = self.tokenizer.encode_plus(input_template, context,
add_special_tokens=True,
add_prefix_space=True,
max_length=MAX_LENGTH,
truncation='only_second',
padding='max_length')
tgt_tokens = self.tokenizer.encode_plus(output_template,
add_special_tokens=True,
add_prefix_space=True,
max_length=MAX_TGT_LENGTH,
truncation=True,
padding='max_length')
processed_ex = {
'doc_key': ex['sent_id'], #this is not unique
'input_token_ids':input_tokens['input_ids'],
'input_attn_mask': input_tokens['attention_mask'],
'tgt_token_ids': tgt_tokens['input_ids'],
'tgt_attn_mask': tgt_tokens['attention_mask'],
}
writer.write(json.dumps(processed_ex) + '\n')
def train_dataloader(self):
if self.hparams.tmp_dir:
data_dir = self.hparams.tmp_dir
else:
data_dir = 'preprocessed_{}'.format(self.hparams.dataset)
dataset = IEDataset(os.path.join(data_dir, 'train.jsonl'))
dataloader = DataLoader(dataset,
pin_memory=True, num_workers=2,
collate_fn=my_collate,
batch_size=self.hparams.train_batch_size,
shuffle=True)
return dataloader
def val_dataloader(self):
if self.hparams.tmp_dir:
data_dir = self.hparams.tmp_dir
else:
data_dir = 'preprocessed_{}'.format(self.hparams.dataset)
dataset = IEDataset(os.path.join(data_dir, 'val.jsonl'))
dataloader = DataLoader(dataset, pin_memory=True, num_workers=2,
collate_fn=my_collate,
batch_size=self.hparams.eval_batch_size, shuffle=False)
return dataloader
def test_dataloader(self):
if self.hparams.tmp_dir:
data_dir = self.hparams.tmp_dir
else:
data_dir = 'preprocessed_{}'.format(self.hparams.dataset)
dataset = IEDataset(os.path.join(data_dir, 'test.jsonl'))
dataloader = DataLoader(dataset, pin_memory=True, num_workers=2,
collate_fn=my_collate,
batch_size=self.hparams.eval_batch_size, shuffle=False)
return dataloader
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--train-file',type=str)
parser.add_argument('--val-file', type=str)
parser.add_argument('--test-file', type=str)
parser.add_argument('--tmp_dir', default='tmp')
parser.add_argument('--train_batch_size', type=int, default=2)
parser.add_argument('--eval_batch_size', type=int, default=4)
parser.add_argument('--mark-trigger', action='store_true', default=True)
parser.add_argument('--dataset', type=str, default='combined')
args = parser.parse_args()
dm = ACEDataModule(args=args)
dm.prepare_data()
# training dataloader
dataloader = dm.train_dataloader()
for idx, batch in enumerate(dataloader):
print(batch)
break
|