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
|
"""
工具模块
提供数据加载、CoNLL 格式处理等工具函数。
"""
import os
import glob
from typing import List, Dict, Any, Tuple
from datasets import Dataset
from tqdm.auto import tqdm
def word_to_char_labels(text: str, word_labels: List[Tuple[str, str]]) -> List[str]:
"""Convert word-level labels to char-level"""
char_labels = ["O"] * len(text)
pos = 0
for token, label in word_labels:
if pos >= len(text):
break
while pos < len(text) and text[pos] != token[0]:
pos += 1
if pos >= len(text):
break
if text[pos: pos + len(token)] == token:
for i in range(len(token)):
idx = pos + i
if idx < len(char_labels):
if i == 0 and label.startswith("B-"):
char_labels[idx] = label
elif label.startswith("B-"):
char_labels[idx] = "I" + label[1:]
else:
char_labels[idx] = label
pos += len(token)
else:
pos += 1
return char_labels
def parse_conll_file(filepath: str) -> List[Dict[str, Any]]:
"""Parse .conll → [{"text": str, "char_labels": List[str]}]"""
with open(filepath, "r", encoding="utf-8") as f:
lines = [line.rstrip("\n") for line in f.readlines()]
# 检测 word-level
is_word_level = any(
len(line.split()[0]) > 1
for line in lines
if line.strip() and not line.startswith("-DOCSTART-") and len(line.split()) >= 4
)
samples = []
if is_word_level:
current_text_parts = []
current_word_labels = []
for line in lines:
if not line or line.startswith("-DOCSTART-"):
if current_text_parts:
text = "".join(current_text_parts)
char_labels = word_to_char_labels(text, current_word_labels)
samples.append({"text": text, "char_labels": char_labels})
current_text_parts = []
current_word_labels = []
continue
parts = line.split()
if len(parts) >= 4:
token, label = parts[0], parts[3]
current_text_parts.append(token)
current_word_labels.append((token, label))
if current_text_parts:
text = "".join(current_text_parts)
char_labels = word_to_char_labels(text, current_word_labels)
samples.append({"text": text, "char_labels": char_labels})
else:
current_text = []
current_labels = []
for line in lines:
if line.startswith("-DOCSTART-"):
if current_text:
samples.append({
"text": "".join(current_text),
"char_labels": current_labels.copy(),
})
current_text, current_labels = [], []
continue
if not line:
if current_text:
samples.append({
"text": "".join(current_text),
"char_labels": current_labels.copy(),
})
current_text, current_labels = [], []
continue
parts = line.split()
if len(parts) >= 4:
char = parts[0].replace("\\n", "\n")
label = parts[3]
current_text.append(char)
current_labels.append(label)
if current_text:
samples.append({
"text": "".join(current_text),
"char_labels": current_labels.copy(),
})
return samples
def load_conll_dataset(conll_dir_or_files: str) -> Tuple[Dataset, List[str]]:
"""Load .conll files → Dataset"""
filepaths = []
if os.path.isdir(conll_dir_or_files):
filepaths = sorted(glob.glob(os.path.join(conll_dir_or_files, "*.conll")))
elif conll_dir_or_files.endswith(".conll"):
filepaths = [conll_dir_or_files]
else:
raise ValueError("conll_dir_or_files must be .conll file or directory")
if not filepaths:
raise FileNotFoundError(f"No .conll files found in {conll_dir_or_files}")
print(f"Loading {len(filepaths)} conll files: {filepaths}")
all_samples = []
label_set = {"O"}
for fp in tqdm(filepaths, desc="Parsing .conll"):
samples = parse_conll_file(fp)
for s in samples:
all_samples.append(s)
label_set.update(s["char_labels"])
# Build label list
label_list = ["O"]
for label in sorted(label_set - {"O"}):
if label.startswith("B-") or label.startswith("I-"):
label_list.append(label)
for label in list(label_list):
if label.startswith("B-"):
i_label = "I" + label[1:]
if i_label not in label_list:
label_list.append(i_label)
print(f"⚠️ Added missing {i_label} for {label}")
print(f"✅ Loaded {len(all_samples)} samples, {len(label_list)} labels: {label_list}")
return Dataset.from_list(all_samples), label_list
def tokenize_and_align_labels(examples, tokenizer, label2id, max_length=128):
"""Tokenize and align labels with tokenizer"""
tokenized = tokenizer(
examples["text"],
truncation=True,
padding=True,
max_length=max_length,
return_offsets_mapping=True,
return_tensors=None,
)
labels = []
for i, label_seq in enumerate(examples["char_labels"]):
offsets = tokenized["offset_mapping"][i]
label_ids = []
for start, end in offsets:
if start == end:
label_ids.append(-100)
else:
label_ids.append(label2id[label_seq[start]])
labels.append(label_ids)
tokenized["labels"] = labels
return tokenized
__all__ = [
"word_to_char_labels",
"parse_conll_file",
"load_conll_dataset",
"tokenize_and_align_labels",
]
|