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104 lines
4.4 KiB
Python
104 lines
4.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from tests.models.language.pooling.embed_utils import (
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correctness_test_embed_models)
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from tests.models.utils import (CLSPoolingEmbedModelInfo,
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CLSPoolingRerankModelInfo, EmbedModelInfo,
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LASTPoolingEmbedModelInfo, RerankModelInfo)
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from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
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MODELS = [
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########## BertModel
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CLSPoolingEmbedModelInfo("BAAI/bge-base-en",
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architecture="BertModel",
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mteb_score=0.779336792,
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enable_test=True),
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CLSPoolingEmbedModelInfo("BAAI/bge-base-zh",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-small-en",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-small-zh",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-large-en",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-large-zh",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-large-zh-noinstruct",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-base-en-v1.5",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-base-zh-v1.5",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-small-en-v1.5",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-small-zh-v1.5",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-large-en-v1.5",
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architecture="BertModel",
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enable_test=False),
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CLSPoolingEmbedModelInfo("BAAI/bge-large-zh-v1.5",
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architecture="BertModel",
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enable_test=False),
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########## XLMRobertaModel
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CLSPoolingEmbedModelInfo("BAAI/bge-m3",
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architecture="XLMRobertaModel",
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mteb_score=0.787343078,
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enable_test=True),
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########## Qwen2Model
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LASTPoolingEmbedModelInfo("BAAI/bge-code-v1",
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architecture="Qwen2Model",
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mteb_score=0.75724465,
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dtype="float32",
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enable_test=True),
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]
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RERANK_MODELS = [
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########## XLMRobertaForSequenceClassification
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CLSPoolingRerankModelInfo(
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"BAAI/bge-reranker-base",
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architecture="XLMRobertaForSequenceClassification",
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mteb_score=0.32398,
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enable_test=True),
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CLSPoolingRerankModelInfo(
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"BAAI/bge-reranker-large",
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architecture="XLMRobertaForSequenceClassification",
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enable_test=False),
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CLSPoolingRerankModelInfo(
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"BAAI/bge-reranker-v2-m3",
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architecture="XLMRobertaForSequenceClassification",
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enable_test=False)
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]
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@pytest.mark.parametrize("model_info", MODELS)
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def test_embed_models_mteb(hf_runner, vllm_runner,
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model_info: EmbedModelInfo) -> None:
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mteb_test_embed_models(hf_runner, vllm_runner, model_info)
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@pytest.mark.parametrize("model_info", MODELS)
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def test_embed_models_correctness(hf_runner, vllm_runner,
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model_info: EmbedModelInfo,
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example_prompts) -> None:
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correctness_test_embed_models(hf_runner, vllm_runner, model_info,
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example_prompts)
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@pytest.mark.parametrize("model_info", RERANK_MODELS)
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def test_rerank_models_mteb(hf_runner, vllm_runner,
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model_info: RerankModelInfo) -> None:
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mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
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