cross-encoder

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ms-marco-MiniLM-L6-v2

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Sentence Transformers - Cross-Encoders 245

Hello!

This pull request has been automatically generated from the export_dynamic_quantized_onnx_model function from the Sentence Transformers library.

Config

QuantizationConfig(
    is_static=False,
    format=<QuantFormat.QOperator: 0>,
    mode=<QuantizationMode.IntegerOps: 0>,
    activations_dtype=<QuantType.QUInt8: 1>,
    activations_symmetric=False,
    weights_dtype=<QuantType.QInt8: 0>,
    weights_symmetric=True,
    per_channel=True,
    reduce_range=False,
    nodes_to_quantize=[],
    nodes_to_exclude=[],
    operators_to_quantize=['Conv',
    'MatMul',
    'Attention',
    'LSTM',
    'Gather',
    'Transpose',
    'EmbedLayerNormalization'],
    qdq_add_pair_to_weight=False,
    qdq_dedicated_pair=False,
    qdq_op_type_per_channel_support_to_axis={'MatMul': 1}
)

Tip:

Consider testing this pull request before merging by loading the model from this PR with the revision argument:

from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
    "cross-encoder/ms-marco-MiniLM-L6-v2",
    revision=f"refs/pr/{pr_number}",
    backend="onnx",
    model_kwargs={"file_name": "model_qint8_avx512_vnni.onnx"},
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)