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"""
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",
]
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