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https://github.com/qdrant/fastembed.git
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860e2ad691
* feat: Added jina reranker models * chore: Added jina reranker canonical score values * chore: added rounding of the output for easier reproducability * chore: Added jina reranker models in batch test * chore: remove redundant np.round * chore: test only <1gb files in local * chore: Updated docs to add rerankers * fix: recompute canonical values with fp16 * new: extend NOTICE with jina reranker v2 --------- Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
import os
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import numpy as np
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import pytest
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from fastembed.rerank.cross_encoder import TextCrossEncoder
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from tests.utils import delete_model_cache
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CANONICAL_SCORE_VALUES = {
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"Xenova/ms-marco-MiniLM-L-6-v2": np.array([8.500708, -2.541011]),
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"Xenova/ms-marco-MiniLM-L-12-v2": np.array([9.330912, -2.0380247]),
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"BAAI/bge-reranker-base": np.array([6.15733337, -3.65939403]),
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"jinaai/jina-reranker-v1-tiny-en": np.array([2.5911, 0.1122]),
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"jinaai/jina-reranker-v1-turbo-en": np.array([1.8295, -2.8908]),
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"jinaai/jina-reranker-v2-base-multilingual": np.array([1.6533, -1.6455]),
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}
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def test_rerank():
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is_ci = os.getenv("CI")
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for model_desc in TextCrossEncoder.list_supported_models():
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if not is_ci and model_desc["size_in_GB"] > 1:
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continue
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model_name = model_desc["model"]
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model = TextCrossEncoder(model_name=model_name)
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query = "What is the capital of France?"
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documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."]
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scores = np.array(list(model.rerank(query, documents)))
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canonical_scores = CANONICAL_SCORE_VALUES[model_name]
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assert np.allclose(
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scores, canonical_scores, atol=1e-3
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), f"Model: {model_name}, Scores: {scores}, Expected: {canonical_scores}"
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if is_ci:
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delete_model_cache(model.model._model_dir)
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@pytest.mark.parametrize(
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"model_name",
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[
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model_desc["model"]
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for model_desc in TextCrossEncoder.list_supported_models()
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if model_desc["size_in_GB"] < 1 and model_desc["model"] in CANONICAL_SCORE_VALUES.keys()
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],
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)
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def test_batch_rerank(model_name):
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is_ci = os.getenv("CI")
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model = TextCrossEncoder(model_name=model_name)
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query = "What is the capital of France?"
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documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."] * 50
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scores = np.array(list(model.rerank(query, documents, batch_size=10)))
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canonical_scores = np.tile(CANONICAL_SCORE_VALUES[model_name], 50)
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assert scores.shape == canonical_scores.shape, f"Unexpected shape for model {model_name}"
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assert np.allclose(
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scores, canonical_scores, atol=1e-3
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), f"Model: {model_name}, Scores: {scores}, Expected: {canonical_scores}"
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if is_ci:
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delete_model_cache(model.model._model_dir)
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@pytest.mark.parametrize(
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"model_name",
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["Xenova/ms-marco-MiniLM-L-6-v2"],
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)
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def test_lazy_load(model_name):
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is_ci = os.getenv("CI")
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model = TextCrossEncoder(model_name=model_name, lazy_load=True)
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assert not hasattr(model.model, "model")
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query = "What is the capital of France?"
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documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."]
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list(model.rerank(query, documents))
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assert hasattr(model.model, "model")
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if is_ci:
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delete_model_cache(model.model._model_dir)
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