ReMe/reme4/components/tokenizer/base_tokenizer.py
jinli.yl 4052ddbe6b up
2026-05-26 17:35:15 +08:00

63 lines
2.3 KiB
Python

"""Abstract base class for tokenizers."""
from abc import abstractmethod
from pathlib import Path
import aiofiles
from ..base_component import BaseComponent
from ...enumeration import ComponentEnum
class BaseTokenizer(BaseComponent):
"""Tokenizer base class with shared stopword loading and post-processing.
Subclasses implement raw tokenization via `_tokenize_one`; lowercasing and
stopword filtering are handled here so every backend behaves consistently.
"""
component_type = ComponentEnum.TOKENIZER
DEFAULT_STOPWORDS_PATH = Path(__file__).parent / "stopwords"
def __init__(
self,
stopwords_path: str | Path | None = None,
filter_stopwords: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.stopwords_path = Path(stopwords_path) if stopwords_path else self.DEFAULT_STOPWORDS_PATH
self.filter_stopwords = filter_stopwords
self._stopwords: set[str] = set()
async def _start(self) -> None:
# A missing file is non-fatal: tokenizers still work, just without filtering.
if not self.stopwords_path.exists():
self.logger.warning(f"Stopwords file not found: {self.stopwords_path}")
return
async with aiofiles.open(self.stopwords_path, encoding="utf-8") as f:
content = await f.read()
self._stopwords = {line.strip().lower() for line in content.splitlines() if line.strip()}
self.logger.info(f"Loaded {len(self._stopwords)} stopwords from {self.stopwords_path}")
async def _close(self) -> None:
self._stopwords.clear()
@property
def stopwords(self) -> set[str]:
return self._stopwords
def tokenize(self, texts: list[str], lower: bool = True, **kwargs) -> list[list[str]]:
"""Tokenize each text and apply shared post-processing."""
return [self._postprocess(self._tokenize_one(t, **kwargs), lower) for t in texts]
def _postprocess(self, tokens: list[str], lower: bool) -> list[str]:
if lower:
tokens = [t.lower() for t in tokens]
if self.filter_stopwords and self._stopwords:
tokens = [t for t in tokens if t not in self._stopwords]
return tokens
@abstractmethod
def _tokenize_one(self, text: str, **kwargs) -> list[str]:
"""Return raw tokens for one text; lowercasing/filtering happen upstream."""