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add expected results for m3 compute_score

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hanhainebula 2024-10-19 21:56:55 +08:00
parent 291dec2f6a
commit 33970b899b
2 changed files with 8 additions and 12 deletions
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10
examples/inference/embedder/encoder_only/m3_multi_devices_compute_score.py
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@ -51,9 +51,7 @@ def test_m3_multi_devices():
if __name__ == '__main__':
test_m3_multi_devices()
# print("--------------------------------")
# print("Expected Output:")
# print("Dense score:")
# print(" [[0.626 0.3477]\n [0.3496 0.678 ]]")
# print("Sparse score:")
# print(" [[0.19554901 0.00880432]\n [0. 0.18036556]]")
print("--------------------------------")
print("Expected Output:")
print("{'colbert': [0.7798609733581543, 0.7897368669509888], 'sparse': [0.1956787109375, 0.1802978515625], 'dense': [0.6259765625, 0.67822265625], 'sparse+dense': [0.5266770720481873, 0.5633169412612915], 'colbert+sparse+dense': [0.6367570757865906, 0.6617604494094849]}")
print("{'colbert': [0.4524071514606476, 0.4619773030281067], 'sparse': [0.0, 0.0087890625], 'dense': [0.349853515625, 0.34765625], 'sparse+dense': [0.2691181004047394, 0.269456148147583], 'colbert+sparse+dense': [0.34880897402763367, 0.3531610071659088]}")

10
examples/inference/embedder/encoder_only/m3_single_device_compute_score.py
View File

@ -51,9 +51,7 @@ def test_m3_single_device():
if __name__ == '__main__':
test_m3_single_device()
# print("--------------------------------")
# print("Expected Output:")
# print("Dense score:")
# print(" [[0.626 0.3477]\n [0.3496 0.678 ]]")
# print("Sparse score:")
# print(" [[0.19554901 0.00880432]\n [0. 0.18036556]]")
print("--------------------------------")
print("Expected Output:")
print("{'colbert': [0.7798250317573547, 0.7899274826049805], 'sparse': [0.195556640625, 0.180419921875], 'dense': [0.6259765625, 0.67822265625], 'sparse+dense': [0.5266488790512085, 0.5633450746536255], 'colbert+sparse+dense': [0.6367254853248596, 0.6618592143058777]}")
print("{'colbert': [0.4524373412132263, 0.46213820576667786], 'sparse': [0.0, 0.0088043212890625], 'dense': [0.349609375, 0.34765625], 'sparse+dense': [0.2689302861690521, 0.26945966482162476], 'colbert+sparse+dense': [0.34871599078178406, 0.3532329499721527]}")