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0c0db8b 8d64e8f 0c0db8b c7804f9 fe216b5 c7804f9 fe216b5 c7804f9 fe216b5 c7804f9 fe216b5 c7804f9 fe216b5 c7804f9 fe216b5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | import argparse, json, torch
from dataclasses import dataclass
from typing import List, Dict, Optional, Tuple
from transformers import AutoTokenizer
from torch.utils.data import Dataset
# ==============================================================
# 1. Data structure
# ==============================================================
@dataclass
class SRLSample:
words: List[str]
predicate_word_idx: int
labels: List[str]
predicate_form: Optional[str] = None
# ==============================================================
# 2. Bracket → BIO conversion (unchanged)
# ==============================================================
def _bio_from_brackets(tags):
bio, stack = [], []
for t in tags:
if "(V*" in t:
bio.append("B-V")
continue
opens = []
i = 0
while True:
s = t.find("(", i)
if s == -1: break
e = t.find("*", s)
if e == -1: break
role = t[s+1:e]
opens.append(role)
i = e + 1
closes = t.count(")")
if opens:
bio.append(f"B-{opens[0]}")
for r in opens: stack.append(r)
elif stack:
bio.append(f"I-{stack[-1]}")
else:
bio.append("O")
for _ in range(closes):
if stack: stack.pop()
return bio
def _read_sentences(path):
sent = []
with open(path, "r", encoding="utf-8", errors="replace") as f:
for line in f:
line = line.rstrip("\n")
if not line:
if sent: yield sent; sent = []
continue
cols = line.split()
if cols: sent.append(cols)
if sent: yield sent
# ==============================================================
# 3. CoNLL → SRLSample objects (in-memory)
# ==============================================================
def load_conll_samples(in_path, word_col_idx=3, srl_first_col_idx=11):
"""
Reads .gold_conll file and returns list[SRLSample],
one per predicate column.
"""
samples = []
for sent in _read_sentences(in_path):
words = [row[word_col_idx] for row in sent]
max_cols = max(len(row) for row in sent)
for srl_col in range(srl_first_col_idx, max_cols):
tags = [row[srl_col] if srl_col < len(row) else "*" for row in sent]
try:
pred_idx = next(i for i, t in enumerate(tags) if "(V*" in t)
except StopIteration:
continue
labels = _bio_from_brackets(tags)
predicate_form = words[pred_idx]
samples.append(SRLSample(words, pred_idx, labels, predicate_form))
print(f"[SRL_preprocessing] Loaded {len(samples)} predicate instances from {in_path}")
return samples
# ==============================================================
# 4. Dataset + Collate (same as yours, lightly cleaned)
# ==============================================================
class SRLDataset(Dataset):
def __init__(self, samples: List[SRLSample], tokenizer: AutoTokenizer,
label2id: Dict[str, int], max_length: int = 256, debug_print=False):
self.samples = samples
self.tokenizer = tokenizer
self.label2id = label2id
self.id2label = {v: k for k, v in label2id.items()}
self.max_length = max_length
self.debug_print = debug_print
def __len__(self): return len(self.samples)
def _tokenize_sentence(self, words):
return self.tokenizer(words, is_split_into_words=True,
add_special_tokens=False, return_attention_mask=False,
return_token_type_ids=False)
def _tokenize_predicate(self, form):
return self.tokenizer(form, add_special_tokens=False,
return_attention_mask=False,
return_token_type_ids=False)
def __getitem__(self, idx):
instance = self.samples[idx]
words = instance.words
n_words = len(words)
pred_form = instance.predicate_form or words[instance.predicate_word_idx]
enc_sent = self._tokenize_sentence(words)
enc_pred = self._tokenize_predicate(pred_form)
sent_wp_ids = enc_sent["input_ids"]
pred_wp_ids = enc_pred["input_ids"]
input_ids = [self.tokenizer.cls_token_id] + sent_wp_ids + [self.tokenizer.sep_token_id] \
+ pred_wp_ids + [self.tokenizer.sep_token_id]
ttids = [0] * (1 + len(sent_wp_ids) + 1) + [1] * (len(pred_wp_ids) + 1)
tmp = self.tokenizer(words, is_split_into_words=True)
word_ids = tmp.word_ids()
first_pos_by_wid = {}
for pos, wid in enumerate(word_ids):
if wid is not None and wid not in first_pos_by_wid:
first_pos_by_wid[wid] = pos
word_first_wp_fullidx = [first_pos_by_wid[w] for w in range(n_words)]
label_ids = [self.label2id[l] for l in instance.labels]
indicator = [0]*n_words; indicator[instance.predicate_word_idx] = 1
attention_mask = [1]*len(input_ids)
if len(input_ids) > self.max_length:
max_pos = self.max_length-1
input_ids = input_ids[:self.max_length]
ttids = ttids[:self.max_length]
attention_mask = attention_mask[:self.max_length]
word_first_wp_fullidx = [min(p, max_pos) for p in word_first_wp_fullidx]
return {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"token_type_ids": torch.tensor(ttids, dtype=torch.long),
"attention_mask": torch.tensor(attention_mask, dtype=torch.long),
"word_first_wp_fullidx": torch.tensor(word_first_wp_fullidx, dtype=torch.long),
"labels": torch.tensor(label_ids, dtype=torch.long),
"indicator": torch.tensor(indicator, dtype=torch.long),
"sent_len": torch.tensor(len(words), dtype=torch.long),
"pred_word_idx": torch.tensor(instance.predicate_word_idx, dtype=torch.long)
}
def srl_collate(batch: List[Dict], pad_token_id: int, pad_label_id: int = -100):
B = len(batch)
max_L = max(item["input_ids"].size(0) for item in batch)
input_ids = torch.full((B, max_L), pad_token_id, dtype=torch.long)
token_type_ids = torch.zeros((B, max_L), dtype=torch.long)
attention_mask = torch.zeros((B, max_L), dtype=torch.long)
max_n = max(int(item["sent_len"]) for item in batch)
word_first_wp_fullidx = torch.full((B, max_n), -1, dtype=torch.long)
labels = torch.full((B, max_n), pad_label_id, dtype=torch.long)
indicator = torch.zeros((B, max_n), dtype=torch.long)
sent_lens = torch.zeros((B,), dtype=torch.long)
pred_word_idx = torch.zeros((B,), dtype=torch.long)
sentence_mask = torch.zeros((B, max_n), dtype=torch.bool)
for i, item in enumerate(batch):
L = item["input_ids"].size(0)
input_ids[i, :L] = item["input_ids"]
token_type_ids[i, :L] = item["token_type_ids"]
attention_mask[i, :L] = item["attention_mask"]
n = int(item["sent_len"])
word_first_wp_fullidx[i, :n] = item["word_first_wp_fullidx"]
labels[i, :n] = item["labels"]
indicator[i, :n] = item["indicator"]
sent_lens[i] = n
pred_word_idx[i] = item["pred_word_idx"]
sentence_mask[i, :n] = True
return {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
"word_first_wp_fullidx": word_first_wp_fullidx,
"sentence_mask": sentence_mask,
"labels": labels,
"indicator": indicator,
"sent_lens": sent_lens,
"pred_word_idx": pred_word_idx,
}
# ==============================================================
# 5. Helper for trainer
# ==============================================================
def data_processing_for_loader_conll(
train_conll: str,
dev_conll: Optional[str],
# test_conll: Optional[str],
tokenizer,
word_col_idx: int = 3,
srl_first_col_idx: int = 11,
max_length: int = 256
) -> Tuple[SRLDataset, Optional[SRLDataset], Dict[str, int], Dict[int, str]]:
"""
Reads train/dev/test .gold_conll files and returns:
train_dataset, dev_dataset, test_dataset, label2id, id2label
* label set is computed from the UNION of train/dev/test labels
* dev/test can be None
"""
# Load samples
train_samples = load_conll_samples(train_conll, word_col_idx, srl_first_col_idx)
dev_samples = load_conll_samples(dev_conll, word_col_idx, srl_first_col_idx) if dev_conll else []
# test_samples = load_conll_samples(test_conll, word_col_idx, srl_first_col_idx) if test_conll else []
# Build label maps from ALL splits
all_samples = train_samples + dev_samples
label2id = {}
for s in all_samples:
for lab in s.labels:
if lab not in label2id:
label2id[lab] = len(label2id)
id2label = {v: k for k, v in label2id.items()}
# Datasets
train_ds = SRLDataset(train_samples, tokenizer, label2id, max_length=max_length)
dev_ds = SRLDataset(dev_samples, tokenizer, label2id, max_length=max_length) if dev_samples else None
# test_ds = SRLDataset(test_samples, tokenizer, label2id, max_length=max_length) if test_samples else None
return train_ds, dev_ds, label2id, id2label
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