Skip to content

new: decouple colbert query and document tokenizer #556

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
joein merged 1 commit into from
Aug 29, 2025
Merged

new: decouple colbert query and document tokenizer #556

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 14 additions & 18 deletions fastembed/late_interaction/colbert.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,9 @@
from typing import Any, Iterable, Optional, Sequence, Type, Union

import numpy as np
from tokenizers import Encoding
from tokenizers import Encoding, Tokenizer

from fastembed.common.preprocessor_utils import load_tokenizer
from fastembed.common.types import NumpyArray
from fastembed.common import OnnxProvider
from fastembed.common.onnx_model import OnnxOutputContext
Expand Down Expand Up @@ -87,23 +88,8 @@ def tokenize(self, documents: list[str], is_doc: bool = True, **kwargs: Any) ->
)

def _tokenize_query(self, query: str) -> list[Encoding]:
assert self.tokenizer is not None
encoded = self.tokenizer.encode_batch([query])
# colbert authors recommend to pad queries with [MASK] tokens for query augmentation to improve performance
if len(encoded[0].ids) < self.MIN_QUERY_LENGTH:
prev_padding = None
if self.tokenizer.padding:
prev_padding = self.tokenizer.padding
self.tokenizer.enable_padding(
pad_token=self.MASK_TOKEN,
pad_id=self.mask_token_id,
length=self.MIN_QUERY_LENGTH,
)
encoded = self.tokenizer.encode_batch([query])
if prev_padding is None:
self.tokenizer.no_padding()
else:
self.tokenizer.enable_padding(**prev_padding)
assert self.query_tokenizer is not None
encoded = self.query_tokenizer.encode_batch([query])
return encoded

def _tokenize_documents(self, documents: list[str]) -> list[Encoding]:
Expand Down Expand Up @@ -183,6 +169,8 @@ def __init__(
self.pad_token_id: Optional[int] = None
self.skip_list: set[int] = set()

self.query_tokenizer: Optional[Tokenizer] = None

if not self.lazy_load:
self.load_onnx_model()

Expand All @@ -195,6 +183,8 @@ def load_onnx_model(self) -> None:
cuda=self.cuda,
device_id=self.device_id,
)
self.query_tokenizer, _ = load_tokenizer(model_dir=self._model_dir)

assert self.tokenizer is not None
self.mask_token_id = self.special_token_to_id[self.MASK_TOKEN]
self.pad_token_id = self.tokenizer.padding["pad_id"]
Expand All @@ -205,6 +195,12 @@ def load_onnx_model(self) -> None:
current_max_length = self.tokenizer.truncation["max_length"]
# ensure not to overflow after adding document-marker
self.tokenizer.enable_truncation(max_length=current_max_length - 1)
self.query_tokenizer.enable_truncation(max_length=current_max_length - 1)
self.query_tokenizer.enable_padding(
pad_token=self.MASK_TOKEN,
pad_id=self.mask_token_id,
length=self.MIN_QUERY_LENGTH,
)

def embed(
self,
Expand Down
Loading