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new: add bge-reranker-v2-m3 (#743)

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George
2026-09-28 23:44:32 +07:00
committed by GitHub
parent 9bf33d5cfd
commit 71facb03a2
2 changed files with 27 additions and 2 deletions
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fastembed/rerank/cross_encoder/onnx_text_cross_encoder.py
+17
View File
@@ -38,6 +38,23 @@ supported_onnx_models: list[BaseModelDescription] = [
sources=ModelSource(hf="BAAI/bge-reranker-base"),
model_file="onnx/model.onnx",
),
BaseModelDescription(
model="BAAI/bge-reranker-v2-m3",
description="Multilingual BGE reranker based on BGE-M3.",
license="apache-2.0",
size_in_GB=2.27,
sources=ModelSource(hf="onnx-community/bge-reranker-v2-m3-ONNX"),
model_file="onnx/model.onnx",
additional_files=["onnx/model.onnx_data"],
),
BaseModelDescription(
model="BAAI/bge-reranker-v2-m3-int8",
description="Multilingual BGE reranker based on BGE-M3 (INT8 ONNX).",
license="apache-2.0",
size_in_GB=0.57,
sources=ModelSource(hf="onnx-community/bge-reranker-v2-m3-ONNX"),
model_file="onnx/model_int8.onnx",
),
BaseModelDescription(
model="jinaai/jina-reranker-v1-tiny-en",
description="Designed for blazing-fast re-ranking with 8K context length and fewer parameters than jina-reranker-v1-turbo-en.",
tests/test_text_cross_encoder.py
+10 -2
View File
@@ -11,6 +11,11 @@ CANONICAL_SCORE_VALUES = {
"Xenova/ms-marco-MiniLM-L-6-v2": np.array([8.500708, -2.541011]),
"Xenova/ms-marco-MiniLM-L-12-v2": np.array([9.330912, -2.0380247]),
"BAAI/bge-reranker-base": np.array([6.15733337, -3.65939403]),
"BAAI/bge-reranker-v2-m3": np.array([8.78182220, -5.46485329]),
"BAAI/bge-reranker-v2-m3-int8": (
np.array([8.59202957, -5.39362288]), # ONNX Runtime 1.27.0
np.array([8.51154804, -5.56898642]), # ONNX Runtime 1.30.0
),
"jinaai/jina-reranker-v1-tiny-en": np.array([2.5911, 0.1122]),
"jinaai/jina-reranker-v1-turbo-en": np.array([1.8295, -2.8908]),
"jinaai/jina-reranker-v2-base-multilingual": np.array([1.6533, -1.6455]),
@@ -67,8 +72,11 @@ def test_rerank(model_cache, model_name: str) -> None:
), f"Model: {model_desc.model}, Scores: {scores}, Scores2: {scores2}"
canonical_scores = CANONICAL_SCORE_VALUES[model_desc.model]
assert np.allclose(
scores, canonical_scores, atol=1e-3
canonical_variants = (
canonical_scores if isinstance(canonical_scores, tuple) else (canonical_scores,)
)
assert any(
np.allclose(scores, expected, atol=1e-3) for expected in canonical_variants
), f"Model: {model_desc.model}, Scores: {scores}, Expected: {canonical_scores}"