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chore: import upstream snapshot with attribution

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wehub-resource-sync
2026-07-13 13:39:21 +08:00
commit bc34f6df14
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examples/inference/reranker/encoder_only/auto_base_multi_devices.py
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import os
from FlagEmbedding import FlagAutoReranker
def test_base_multi_devices():
model = FlagAutoReranker.from_finetuned(
'BAAI/bge-reranker-large',
use_fp16=True,
batch_size=128,
query_max_length=256,
max_length=512,
devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"]
cache_dir=os.getenv('HF_HUB_CACHE', None),
)
pairs = [
["What is the capital of France?", "Paris is the capital of France."],
["What is the capital of France?", "The population of China is over 1.4 billion people."],
["What is the population of China?", "Paris is the capital of France."],
["What is the population of China?", "The population of China is over 1.4 billion people."]
] * 100
scores = model.compute_score(pairs)
print(scores[:4])
if __name__ == '__main__':
test_base_multi_devices()
print("--------------------------------")
print("Expected Output:")
print("[ 7.97265625 -6.8515625 -7.15625 5.45703125]")
examples/inference/reranker/encoder_only/auto_base_single_device.py
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import os
from FlagEmbedding import FlagAutoReranker
def test_base_multi_devices():
model = FlagAutoReranker.from_finetuned(
'BAAI/bge-reranker-large',
use_fp16=True,
batch_size=128,
query_max_length=256,
max_length=512,
devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]
cache_dir=os.getenv('HF_HUB_CACHE', None),
)
pairs = [
["What is the capital of France?", "Paris is the capital of France."],
["What is the capital of France?", "The population of China is over 1.4 billion people."],
["What is the population of China?", "Paris is the capital of France."],
["What is the population of China?", "The population of China is over 1.4 billion people."]
] * 100
scores = model.compute_score(pairs)
print(scores[:4])
if __name__ == '__main__':
test_base_multi_devices()
print("--------------------------------")
print("Expected Output:")
print("[7.9765625, -6.84375, -7.15625, 5.453125]")
examples/inference/reranker/encoder_only/base_multi_devices.py
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import os
from FlagEmbedding import FlagReranker
def test_base_multi_devices():
model = FlagReranker(
'BAAI/bge-reranker-large',
use_fp16=True,
batch_size=128,
query_max_length=256,
max_length=512,
devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"]
cache_dir=os.getenv('HF_HUB_CACHE', None),
)
pairs = [
["What is the capital of France?", "Paris is the capital of France."],
["What is the capital of France?", "The population of China is over 1.4 billion people."],
["What is the population of China?", "Paris is the capital of France."],
["What is the population of China?", "The population of China is over 1.4 billion people."]
] * 100
scores = model.compute_score(pairs)
print(scores[:4])
if __name__ == '__main__':
test_base_multi_devices()
print("--------------------------------")
print("Expected Output:")
print("[ 7.97265625 -6.8515625 -7.15625 5.45703125]")
examples/inference/reranker/encoder_only/base_single_device.py
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import os
from FlagEmbedding import FlagReranker
def test_base_multi_devices():
model = FlagReranker(
'BAAI/bge-reranker-large',
use_fp16=True,
batch_size=128,
query_max_length=256,
max_length=512,
devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]
cache_dir=os.getenv('HF_HUB_CACHE', None),
)
pairs = [
["What is the capital of France?", "Paris is the capital of France."],
["What is the capital of France?", "The population of China is over 1.4 billion people."],
["What is the population of China?", "Paris is the capital of France."],
["What is the population of China?", "The population of China is over 1.4 billion people."]
] * 100
scores = model.compute_score(pairs)
print(scores[:4])
if __name__ == '__main__':
test_base_multi_devices()
print("--------------------------------")
print("Expected Output:")
print("[7.9765625, -6.84375, -7.15625, 5.453125]")