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import logging
import os
import os.path as osp
import json
import numpy as np
# from konlpy.tag import Okt
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from utils import seed_worker
import pprint
def load_data(args,
config=None, config_kor=None, config_han=None,
tokenizer=None, tokenizer_kor=None, tokenizer_han=None,
split="train"):
if args.joint:
dataset = JointDataset(args, config_kor, config_han, tokenizer_kor, tokenizer_han, split)
else:
assert args.language in ['korean', 'hanja']
if args.language == 'korean':
dataset = KoreanDataset(args, config, tokenizer, split)
elif args.language == 'hanja':
dataset = HanjaDataset(args, config, tokenizer, split)
if split == "train":
dataloader = DataLoader(dataset,
batch_size=args.train_batch_size,
collate_fn=dataset.collate_fn,
worker_init_fn=seed_worker,
num_workers=args.num_workers,
shuffle=True,
drop_last=True,
pin_memory=True)
elif split == "valid":
dataloader = DataLoader(dataset,
batch_size=args.eval_batch_size,
collate_fn=dataset.collate_fn,
shuffle=False,
drop_last=False,
pin_memory=True)
elif split =="test":
dataloader = DataLoader(dataset,
batch_size=args.test_batch_size,
collate_fn=dataset.collate_fn,
shuffle=False,
drop_last=False)
else:
raise ValueError("Data split must be either train/valid/test.")
return dataloader
class JointDataset(Dataset):
def __init__(self, args, config_kor, config_han, tokenizer_kor, tokenizer_han, split="train"):
self.args = args
self.config_kor = config_kor
self.config_han = config_han
self.tokenizer_kor = tokenizer_kor
self.tokenizer_han = tokenizer_han
self.split = split
self.features = []
if args.add_emb:
self.save_dir = osp.join(args.data_dir, f"joint_add_{args.w_kor_emb}")
else:
self.save_dir = osp.join(args.data_dir, "joint_concat")
self.save_path = osp.join(self.save_dir, f"{args.model_type}+{args.model2_type}_{split}.pt")
os.makedirs(self.save_dir, exist_ok=True)
map_dir = '/'.join(args.data_dir.split('/')[:-1])
with open(osp.join(map_dir, "ner_map.json")) as f:
self.ner_map = json.load(f)
with open(osp.join(map_dir, "label_map.json")) as f:
self.label_map = json.load(f)
self.load_and_cache_examples()
def load_and_cache_examples(self):
if osp.exists(self.save_path):
logging.info(f"Loading features from {self.save_path}")
self.features = torch.load(self.save_path)
return
cls_token_kor = self.tokenizer_kor.cls_token
sep_token_kor = self.tokenizer_kor.sep_token
cls_token_han = self.tokenizer_han.cls_token
sep_token_han = self.tokenizer_han.sep_token
num_special_tokens = 2
num_empty_entity_examples = 0
num_empty_label_examples = 0
num_filtered_labels = 0
logging.info(f"Creating features from {self.args.data_dir}")
rootdir = osp.join(self.args.data_dir, f"{self.split}")
N_data_problems = 0
for json_file in tqdm(os.listdir(rootdir), desc="Converting examples to features"):
with open(osp.join(rootdir, json_file), encoding='utf-8') as f:
ex = json.load(f)
if len(ex["entity"]) == 0:
num_empty_entity_examples += 1
continue
if len(ex["relation"]) == 0:
num_empty_label_examples += 1
continue
### Tokenize text & cluster entity mentions ###
entities_kor = [] # list of lists clustering same entity mentions
entities_han = []
coref_dict_kor = {} # { coref_type: entity_idx } -> will be used to cluster mentions
coref_dict_han = {}
ent2idx_kor = {} # { info: entity_idx } -> map entity to idx
ent2idx_han = {}
ent_idx_kor = 0 # unique entity idx
ent_idx_han = 0
prev_idx_kor = 1 # skip cls_token idx
prev_idx_han = 1
input_tokens_kor = [cls_token_kor]
input_tokens_han = [cls_token_han]
long_seq = False
for ent in ex["entity"]:
if (ent["kor"]["type"] == "START" or ent["kor"]["text"] == "" or ent["kor"]["text"] == " " or
ent["han"]["type"] == "START" or ent["han"]["text"] == "" or ent["han"]["text"] == " "):
continue
if ent["han"]["coref_type"] != ent["kor"]["coref_type"]:
ent["han"]["coref_type"] = ent["kor"]["coref_type"]
# when tokenizing, make note of subword idxes
prev_text_kor = ex["text"]["kor"][prev_idx_kor:ent["kor"]["start"]]
prev_text_han = ex["text"]["han"][prev_idx_han:ent["han"]["start"]]
prev_tokens_kor = self.tokenizer_kor.tokenize(prev_text_kor)
prev_tokens_han = self.tokenizer_han.tokenize(prev_text_han)
input_tokens_kor += prev_tokens_kor
input_tokens_han += prev_tokens_han
start_kor = len(input_tokens_kor)
start_han = len(input_tokens_han)
ent_text_kor = ex["text"]["kor"][ent["kor"]["start"]:ent["kor"]["end"]]
ent_text_han = ex["text"]["han"][ent["han"]["start"]:ent["han"]["end"]]
ent_tokens_kor = self.tokenizer_kor.tokenize(ent_text_kor)
ent_tokens_han = self.tokenizer_han.tokenize(ent_text_han)
if self.args.mark_entities:
ent_tokens_kor = ["*"] + ent_tokens_kor + ["*"]
ent_tokens_han = ["*"] + ent_tokens_han + ["*"]
input_tokens_kor += ent_tokens_kor
input_tokens_han += ent_tokens_han
end_kor = len(input_tokens_kor)
end_han = len(input_tokens_han)
prev_idx_kor = ent["kor"]["end"]
prev_idx_han = ent["han"]["end"]
if (start_kor > self.args.max_seq_length-num_special_tokens or
end_kor > self.args.max_seq_length-num_special_tokens or
start_han > self.args.max_seq_length-num_special_tokens or
end_han > self.args.max_seq_length-num_special_tokens):
long_seq = True
break
ent_info_kor = (ent["kor"]["text"], ent["kor"]["start"], ent["kor"]["end"])
ent_info_han = (ent["han"]["text"], ent["han"]["start"], ent["han"]["end"])
full_ent_info_kor = (ent["kor"]["text"], ent["kor"]["start"], ent["kor"]["end"], start_kor, end_kor)
full_ent_info_han = (ent["han"]["text"], ent["han"]["start"], ent["han"]["end"], start_han, end_han)
if ent["kor"]["coref_type"]:
if ent["kor"]["coref_type"] in coref_dict_kor:
coref_idx = coref_dict_kor[ent["kor"]["coref_type"]]
ent2idx_kor[ent_info_kor] = coref_idx
entities_kor[coref_idx].append(full_ent_info_kor)
else:
coref_dict_kor[ent["kor"]["coref_type"]] = ent_idx_kor
ent2idx_kor[ent_info_kor] = ent_idx_kor
entities_kor.append([full_ent_info_kor])
ent_idx_kor += 1
else:
ent2idx_kor[ent_info_kor] = ent_idx_kor
entities_kor.append([full_ent_info_kor])
ent_idx_kor += 1
if ent["han"]["coref_type"]:
if ent["han"]["coref_type"] in coref_dict_han:
coref_idx = coref_dict_han[ent["han"]["coref_type"]]
ent2idx_han[ent_info_han] = coref_idx
entities_han[coref_idx].append(full_ent_info_han)
else:
coref_dict_han[ent["han"]["coref_type"]] = ent_idx_han
ent2idx_han[ent_info_han] = ent_idx_han
entities_han.append([full_ent_info_han])
ent_idx_han += 1
else:
ent2idx_han[ent_info_han] = ent_idx_han
entities_han.append([full_ent_info_han])
ent_idx_han += 1
if not long_seq:
remaining_text_kor = ex["text"]["kor"][prev_idx_kor:]
remaining_text_han = ex["text"]["han"][prev_idx_han:]
input_tokens_kor += self.tokenizer_kor.tokenize(remaining_text_kor)
input_tokens_han += self.tokenizer_han.tokenize(remaining_text_han)
input_tokens_kor = input_tokens_kor[:self.args.max_seq_length - 1]
input_tokens_han = input_tokens_han[:self.args.max_seq_length - 1]
input_tokens_kor += [sep_token_kor]
input_tokens_han += [sep_token_han]
input_ids_kor = self.tokenizer_kor.convert_tokens_to_ids(input_tokens_kor)
input_ids_han = self.tokenizer_han.convert_tokens_to_ids(input_tokens_han)
# Pad to max length
input_ids_kor += [self.config_kor.pad_token_id] * (self.args.max_seq_length - len(input_ids_kor))
input_ids_han += [self.config_han.pad_token_id] * (self.args.max_seq_length - len(input_ids_han))
assert len(input_ids_kor) == len(input_ids_han) == self.args.max_seq_length
### entity masks & NERs
ent_pos_kor, ent_pos_han = [], []
for ent in entities_kor:
ent_pos_kor.append([])
for ment in ent:
token_start, token_end = ment[3], ment[4]
ent_pos_kor[-1].append((token_start, token_end))
for ent in entities_han:
ent_pos_han.append([])
for ment in ent:
token_start, token_end = ment[3], ment[4]
ent_pos_han[-1].append((token_start, token_end))
# debug
for ent_k, ent_h in zip(ent_pos_kor, ent_pos_han):
assert len(ent_k) == len(ent_h)
# print(json_file)
# pprint.pprint(ex["entity"])
# print(entities_kor)
# print(entities_han)
# break
### labels ###
labels = torch.zeros((len(entities_kor), len(entities_kor), self.config_kor.num_labels), dtype=torch.float32)
for relation in ex["relation"]:
s1, o1 = relation["kor"]['subject_entity'], relation["kor"]['object_entity']
s2, o2 = relation["han"]['subject_entity'], relation["han"]['object_entity']
h_idx = ent2idx_kor.get((s1["text"], s1["start"], s1["end"]), None)
t_idx = ent2idx_kor.get((o1["text"], o1["start"], o1["end"]), None)
h_idx2 = ent2idx_han.get((s2["text"], s2["start"], s2["end"]), None)
t_idx2 = ent2idx_han.get((o2["text"], o2["start"], o2["end"]), None)
if h_idx is None or t_idx is None:
num_filtered_labels += 1
continue
# TODO: idx has to match across languages, otherwise the label won't be universal.
# if h_idx != h_idx2 or t_idx != t_idx2:
# import pdb; pdb.set_trace()
# assert h_idx == h_idx2 and t_idx == t_idx2
# debugging
if not( h_idx == h_idx2 and t_idx == t_idx2) :
# print(f"fname: {json_file}")
# pprint.pprint(relation)
N_data_problems += 1
continue
r_idx = self.label_map[relation["kor"]["label"]]
labels[h_idx, t_idx, r_idx] = 1
for h in range(len(entities_kor)):
for t in range(len(entities_kor)):
if torch.all(labels[h][t] == 0):
labels[h][t][0] = 1
self.features.append({
"input_ids_kor": input_ids_kor,
"input_ids_han": input_ids_han,
"ent_pos_kor": ent_pos_kor,
"ent_pos_han": ent_pos_han,
"labels": labels,
"entities_kor": entities_kor,
"entities_han": entities_han,
"text_kor": ex["text"]["kor"],
"text_han": ex["text"]["han"]
})
# self.features.append({
# "input_ids_kor": input_ids_kor,
# "input_ids_han": input_ids_han,
# "ent_pos_kor": ent_pos_kor,
# "ent_pos_han": ent_pos_han,
# "labels": labels
# })
print(f"# problems in (h_idx == h_idx2 and t_idx == t_idx2) : {N_data_problems}")
logging.info(f"# of empty entity examples filtered: {num_empty_entity_examples}")
logging.info(f"# of empty label examples filtered: {num_empty_label_examples}")
logging.info(f"# of beyond-truncated-text labels filtered: {num_filtered_labels}")
logging.info(f"Saving features to {self.save_path}")
torch.save(self.features, self.save_path)
def collate_fn(self, samples):
input_ids_kor = [x["input_ids_kor"] for x in samples]
input_ids_han = [x["input_ids_han"] for x in samples]
ent_pos_kor = [x["ent_pos_kor"] for x in samples]
ent_pos_han = [x["ent_pos_han"] for x in samples]
labels = [x["labels"].view(-1, self.config_kor.num_labels) for x in samples]
input_ids_kor = torch.tensor(input_ids_kor, dtype=torch.long)
input_ids_han = torch.tensor(input_ids_han, dtype=torch.long)
labels = torch.cat(labels, dim=0)
if not self.args.do_analysis:
return {"input_ids_kor": input_ids_kor,
"input_ids_han": input_ids_han,
"ent_pos_kor": ent_pos_kor,
"ent_pos_han": ent_pos_han,
"labels": labels}
elif self.args.do_analysis:
entities_kor = [x["entities_kor"] for x in samples]
entities_han = [x["entities_han"] for x in samples]
text_kor = [x["text_kor"] for x in samples]
text_han = [x["text_han"] for x in samples]
return {"input_ids_kor": input_ids_kor,
"input_ids_han": input_ids_han,
"ent_pos_kor": ent_pos_kor,
"ent_pos_han": ent_pos_han,
"labels": labels,
"entities_kor": entities_kor,
"entities_han": entities_han,
"text_kor": text_kor,
"text_han": text_han
}
def __len__(self):
return len(self.features)
def __getitem__(self, idx):
return self.features[idx]
class KoreanDataset(Dataset):
def __init__(self, args, config, tokenizer, split="train"):
self.args = args
self.config = config
self.tokenizer = tokenizer
self.split = split
self.features = []
# self.word_tokenizer = Okt()
self.save_dir = osp.join(args.data_dir, args.language)
self.save_path = osp.join(self.save_dir, f"{args.model_type}_{split}.pt")
os.makedirs(self.save_dir, exist_ok=True)
map_dir = '/'.join(args.data_dir.split('/')[:-1])
with open(osp.join(map_dir, "ner_map.json")) as f:
self.ner_map = json.load(f)
with open(osp.join(map_dir, "label_map.json")) as f:
self.label_map = json.load(f)
self.load_and_cache_examples()
def load_and_cache_examples(self):
if osp.exists(self.save_path):
logging.info(f"Loading features from {self.save_path}")
self.features = torch.load(self.save_path)
return
cls_token = self.tokenizer.cls_token
sep_token = self.tokenizer.sep_token
num_special_tokens = 2
num_empty_entity_examples = 0
num_empty_label_examples = 0
num_filtered_labels = 0
logging.info(f"Creating features from {self.args.data_dir}")
rootdir = osp.join(self.args.data_dir, f"{self.split}")
# print(f"Current directory: {rootdir}")
for json_file in tqdm(os.listdir(rootdir), desc="Converting examples to features"):
with open(osp.join(rootdir, json_file), encoding='utf-8') as f:
ex = json.load(f)
if len(ex["entity"]) == 0:
num_empty_entity_examples += 1
continue
if len(ex["relation"]) == 0:
num_empty_label_examples += 1
continue
### Tokenize text & cluster entity mentions ###
entities = [] # list of lists clustering same entity mentions
coref_dict = {} # { coref_type: entity_idx } -> will be used to cluster mentions
ent2idx = {} # { info: entity_idx } -> map entity to idx
ent_idx = 0 # unique entity idx
prev_idx = 1 # skip cls_token idx
input_tokens = [cls_token]
long_seq = False
for ent in ex["entity"]:
ent = ent['kor']
if ent["type"] == "START" or ent["text"] == "" or ent["text"] == " ":
continue
# when tokenizing, make note of subword idxes
prev_text = ex["text"]["kor"][prev_idx:ent["start"]]
prev_tokens = self.tokenizer.tokenize(prev_text)
input_tokens += prev_tokens
start = len(input_tokens)
ent_text = ex["text"]["kor"][ent["start"]:ent["end"]]
ent_tokens = self.tokenizer.tokenize(ent_text)
if self.args.mark_entities:
ent_tokens = ["*"] + ent_tokens + ["*"]
input_tokens += ent_tokens
end = len(input_tokens)
prev_idx = ent["end"]
# Skip entity mentions that appear beyond the truncated text
if (start > self.args.max_seq_length-num_special_tokens or
end > self.args.max_seq_length-num_special_tokens):
long_seq = True
break
# this tuple will be used to identify entity
ent_info = (ent["text"], ent["start"], ent["end"], ent["type"])
full_ent_info = (ent["text"], ent["start"], ent["end"], start, end, ent["type"])
if ent["coref_type"]:
if ent["coref_type"] in coref_dict:
coref_idx = coref_dict[ent["coref_type"]]
ent2idx[ent_info] = coref_idx
entities[coref_idx].append(full_ent_info)
else:
coref_dict[ent["coref_type"]] = ent_idx
ent2idx[ent_info] = ent_idx
entities.append([full_ent_info])
ent_idx += 1
else:
ent2idx[ent_info] = ent_idx
entities.append([full_ent_info])
ent_idx += 1
if not long_seq:
remaining_text = ex["text"]["kor"][prev_idx:]
input_tokens += self.tokenizer.tokenize(remaining_text)
input_tokens = input_tokens[:self.args.max_seq_length - 1] # truncation
input_tokens += [sep_token]
input_ids = self.tokenizer.convert_tokens_to_ids(input_tokens)
# Pad to max length to enable sparse attention in bigbird
input_ids += [self.config.pad_token_id] * (self.args.max_seq_length - len(input_ids))
assert len(input_ids) == self.args.max_seq_length
### entity masks & NERs
ent_pos, ent_ner = [], []
for ent in entities:
ent_pos.append([])
# ent_ner.append([])
for ment in ent:
token_start, token_end = ment[3], ment[4]
ent_pos[-1].append((token_start, token_end))
# ent_ner[-1].append(ment[-1])
# ent_masks, ent_ners = [], []
# for ent in entities:
# ent_mask = np.zeros(len(input_ids), dtype=np.float32)
# ent_ner = np.zeros(len(input_ids), dtype=np.float32)
# for ment in ent:
# start, end = ment[3], ment[4]
# # Skip entity mentions that appear beyond the truncated text
# if (start > self.args.max_seq_length-num_special_tokens or
# end > self.args.max_seq_length-num_special_tokens):
# continue
# ent_mask[start:end] = 1
# ent_ner[start:end] = self.ner_map[ment[5]]
# assert ent_mask.sum() != 0
# ent_masks.append(ent_mask)
# ent_ners.append(ent_ner)
# ent_masks = np.stack(ent_masks, axis=0)
# ent_ners = np.stack(ent_ners, axis=0)
### labels ###
labels = torch.zeros((len(entities), len(entities), self.config.num_labels), dtype=torch.float32)
for relation in ex["relation"]:
relation = relation['kor']
s, o = relation['subject_entity'], relation['object_entity']
h_idx = ent2idx.get((s["text"], s["start"], s["end"], s["type"]), None)
t_idx = ent2idx.get((o["text"], o["start"], o["end"], o["type"]), None)
if h_idx is None or t_idx is None:
num_filtered_labels += 1
continue
r_idx = self.label_map[relation["label"]]
labels[h_idx, t_idx, r_idx] = 1
for h in range(len(entities)):
for t in range(len(entities)):
if torch.all(labels[h][t] == 0):
labels[h][t][0] = 1
### label mask ###
# label_mask = np.ones((len(entities), len(entities)), dtype='bool')
# np.fill_diagonal(label_mask, 0) # ignore diagonals
# TODO: normalize ent_masks (test normalization vs. not)
# ent_masks = ent_masks / np.expand_dims(ent_masks.sum(1), axis=1)
self.features.append({
"input_ids": input_ids,
"ent_pos": ent_pos,
"labels": labels,
})
# self.features.append({
# "input_ids": input_ids,
# "ent_masks": ent_masks,
# "ent_ners": ent_ners,
# "labels": labels,
# "label_mask": label_mask
# })
logging.info(f"# of empty entity examples filtered: {num_empty_entity_examples}")
logging.info(f"# of empty label examples filtered: {num_empty_label_examples}")
logging.info(f"# of beyond-truncated-text labels filtered: {num_filtered_labels}")
logging.info(f"Saving features to {self.save_path}")
torch.save(self.features, self.save_path)
def collate_fn(self, samples):
input_ids = [x["input_ids"] for x in samples]
ent_pos = [x["ent_pos"] for x in samples]
# max_ent_len = max([len(x["ent_pos"]) for x in samples])
# ent_masks = [F.pad(torch.from_numpy(x["ent_masks"]), \
# (0, 0, 0, max_ent_len-x["ent_masks"].shape[0])) for x in samples]
# ent_ners = [F.pad(torch.from_numpy(x["ent_ners"]), \
# (0, 0, 0, max_ent_len-x["ent_ners"].shape[0])) for x in samples]
labels = [x["labels"].view(-1, self.config.num_labels) for x in samples]
# labels = [F.pad(torch.from_numpy(x["labels"]), \
# (0, 0, 0, max_ent_len-x["labels"].shape[0], 0, max_ent_len-x["labels"].shape[1]), value=-100) for x in samples]
# label_mask = [F.pad(torch.from_numpy(x["label_mask"]), \
# (0, max_ent_len-x["label_mask"].shape[0], 0, max_ent_len-x["label_mask"].shape[1])) for x in samples]
input_ids = torch.tensor(input_ids, dtype=torch.long)
# ent_masks = torch.stack(ent_masks, dim=0)
labels = torch.cat(labels, dim=0)
# labels = torch.stack(labels, dim=0)
# label_mask = torch.stack(label_mask, dim=0)
return {"input_ids": input_ids,
"ent_pos": ent_pos,
# "ent_masks": ent_masks,
# "ent_ners": ent_ners,
"labels": labels,
# "label_mask": label_mask,
}
def __len__(self):
return len(self.features)
def __getitem__(self, idx):
return self.features[idx]
class HanjaDataset(Dataset):
def __init__(self, args, config, tokenizer, split="train"):
self.args = args
self.config = config
self.tokenizer = tokenizer
self.split = split
self.features = []
self.save_dir = osp.join(args.data_dir, args.language)
self.save_path = osp.join(self.save_dir, f"{args.model_type}_{split}.pt")
os.makedirs(self.save_dir, exist_ok=True)
map_dir = '/'.join(args.data_dir.split('/')[:-1])
with open(osp.join(map_dir, "ner_map.json")) as f:
self.ner_map = json.load(f)
with open(osp.join(map_dir, "label_map.json")) as f:
self.label_map = json.load(f)
self.load_and_cache_examples()
def load_and_cache_examples(self):
if osp.exists(self.save_path):
logging.info(f"Loading features from {self.save_path}")
self.features = torch.load(self.save_path)
return
cls_token = self.tokenizer.cls_token
sep_token = self.tokenizer.sep_token
num_special_tokens = 2
num_empty_entity_examples = 0
num_empty_label_examples = 0
num_filtered_labels = 0
logging.info(f"Creating features from {self.args.data_dir}")
rootdir = osp.join(self.args.data_dir, f"{self.split}")
# print(f"Current directory: {rootdir}")
for json_file in tqdm(os.listdir(rootdir), desc="Converting examples to features"):
with open(osp.join(rootdir, json_file), encoding='utf-8') as f:
ex = json.load(f)
if len(ex["entity"]) == 0:
num_empty_entity_examples += 1
continue
if len(ex["relation"]) == 0:
num_empty_label_examples += 1
continue
### Tokenize text & cluster entity mentions ###
entities = [] # list of lists clustering same entity mentions
coref_dict = {} # { coref_type: entity_idx } -> will be used to cluster mentions
ent2idx = {} # { info: entity_idx } -> map entity to idx
ent_idx = 0 # unique entity idx
prev_idx = 1 # skip cls_token idx
input_tokens = [cls_token]
long_seq = False
for ent in ex["entity"]:
ent = ent['han']
if ent["type"] == "START" or ent["text"] == "" or ent["text"] == " ":
continue
# when tokenizing, make note of subword idxes
prev_text = ex["text"]['han'][prev_idx:ent["start"]]
prev_tokens = self.tokenizer.tokenize(prev_text)
input_tokens += prev_tokens
start = len(input_tokens)
ent_text = ex["text"]['han'][ent["start"]:ent["end"]]
ent_tokens = self.tokenizer.tokenize(ent_text)
if self.args.mark_entities:
ent_tokens = ["*"] + ent_tokens + ["*"]
input_tokens += ent_tokens
end = len(input_tokens)
prev_idx = ent["end"]
# Skip entity mentions that appear beyond the truncated text
if (start > self.args.max_seq_length-num_special_tokens or
end > self.args.max_seq_length-num_special_tokens):
long_seq = True
break
# this tuple will be used to identify entity
ent_info = (ent["text"], ent["start"], ent["end"], ent["type"])
full_ent_info = (ent["text"], ent["start"], ent["end"], start, end, ent["type"])
if ent["coref_type"]:
if ent["coref_type"] in coref_dict:
coref_idx = coref_dict[ent["coref_type"]]
ent2idx[ent_info] = coref_idx
entities[coref_idx].append(full_ent_info)
else:
coref_dict[ent["coref_type"]] = ent_idx
ent2idx[ent_info] = ent_idx
entities.append([full_ent_info])
ent_idx += 1
else:
ent2idx[ent_info] = ent_idx
entities.append([full_ent_info])
ent_idx += 1
if not long_seq:
remaining_text = ex["text"]['han'][prev_idx:]
input_tokens += self.tokenizer.tokenize(remaining_text)
input_tokens = input_tokens[:self.args.max_seq_length - 1] # truncation
input_tokens += [sep_token]
input_ids = self.tokenizer.convert_tokens_to_ids(input_tokens)
# Pad to max length to enable sparse attention in bigbird
input_ids += [self.config.pad_token_id] * (self.args.max_seq_length - len(input_ids))
assert len(input_ids) == self.args.max_seq_length
### entity masks & NERs
ent_pos, ent_ner = [], []
for ent in entities:
ent_pos.append([])
# ent_ner.append([])
for ment in ent:
token_start, token_end = ment[3], ment[4]
ent_pos[-1].append((token_start, token_end))
# ent_ner[-1].append(ment[-1])
# ent_masks, ent_ners = [], []
# for ent in entities:
# ent_mask = np.zeros(len(input_ids), dtype=np.float32)
# ent_ner = np.zeros(len(input_ids), dtype=np.float32)
# for ment in ent:
# start, end = ment[3], ment[4]
# # Skip entity mentions that appear beyond the truncated text
# if (start > self.args.max_seq_length-num_special_tokens or
# end > self.args.max_seq_length-num_special_tokens):
# continue
# ent_mask[start:end] = 1
# ent_ner[start:end] = self.ner_map[ment[5]]
# assert ent_mask.sum() != 0
# ent_masks.append(ent_mask)
# ent_ners.append(ent_ner)
# ent_masks = np.stack(ent_masks, axis=0)
# ent_ners = np.stack(ent_ners, axis=0)
### labels ###
labels = torch.zeros((len(entities), len(entities), self.config.num_labels), dtype=torch.float32)
for relation in ex["relation"]:
r_idx = self.label_map[relation["label"]]
relation = relation['han']
s, o = relation['subject_entity'], relation['object_entity']
h_idx = ent2idx.get((s["text"], s["start"], s["end"], s["type"]), None)
t_idx = ent2idx.get((o["text"], o["start"], o["end"], o["type"]), None)
if h_idx is None or t_idx is None:
num_filtered_labels += 1
continue
labels[h_idx, t_idx, r_idx] = 1
for h in range(len(entities)):
for t in range(len(entities)):
if torch.all(labels[h][t] == 0):
labels[h][t][0] = 1
### label mask ###
# label_mask = np.ones((len(entities), len(entities)), dtype='bool')
# np.fill_diagonal(label_mask, 0) # ignore diagonals
# TODO: normalize ent_masks (test normalization vs. not)
# ent_masks = ent_masks / np.expand_dims(ent_masks.sum(1), axis=1)
self.features.append({
"input_ids": input_ids,
"ent_pos": ent_pos,
"labels": labels,
})
# self.features.append({
# "input_ids": input_ids,
# "ent_masks": ent_masks,
# "ent_ners": ent_ners,
# "labels": labels,
# "label_mask": label_mask
# })
logging.info(f"# of empty entity examples filtered: {num_empty_entity_examples}")
logging.info(f"# of empty label examples filtered: {num_empty_label_examples}")
logging.info(f"# of beyond-truncated-text labels filtered: {num_filtered_labels}")
logging.info(f"Saving features to {self.save_path}")
torch.save(self.features, self.save_path)
def collate_fn(self, samples):
input_ids = [x["input_ids"] for x in samples]
ent_pos = [x["ent_pos"] for x in samples]
# max_ent_len = max([len(x["ent_pos"]) for x in samples])
# ent_masks = [F.pad(torch.from_numpy(x["ent_masks"]), \
# (0, 0, 0, max_ent_len-x["ent_masks"].shape[0])) for x in samples]
# ent_ners = [F.pad(torch.from_numpy(x["ent_ners"]), \
# (0, 0, 0, max_ent_len-x["ent_ners"].shape[0])) for x in samples]
labels = [x["labels"].view(-1, self.config.num_labels) for x in samples]
# labels = [F.pad(torch.from_numpy(x["labels"]), \
# (0, 0, 0, max_ent_len-x["labels"].shape[0], 0, max_ent_len-x["labels"].shape[1]), value=-100) for x in samples]
# label_mask = [F.pad(torch.from_numpy(x["label_mask"]), \
# (0, max_ent_len-x["label_mask"].shape[0], 0, max_ent_len-x["label_mask"].shape[1])) for x in samples]
input_ids = torch.tensor(input_ids, dtype=torch.long)
# ent_masks = torch.stack(ent_masks, dim=0)
labels = torch.cat(labels, dim=0)
# labels = torch.stack(labels, dim=0)
# label_mask = torch.stack(label_mask, dim=0)
return {"input_ids": input_ids,
"ent_pos": ent_pos,
# "ent_masks": ent_masks,
# "ent_ners": ent_ners,
"labels": labels,
# "label_mask": label_mask,
}
def __len__(self):
return len(self.features)
def __getitem__(self, idx):
return self.features[idx] |