cross-encoder

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stsb-roberta-base

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Sentence Transformers - Cross-Encoders 106

Hello!

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

Config

OptimizationConfig(
    optimization_level=2,
    optimize_for_gpu=False,
    fp16=False,
    optimize_with_onnxruntime_only=None,
    enable_transformers_specific_optimizations=True,
    disable_gelu=None,
    disable_gelu_fusion=False,
    disable_layer_norm=None,
    disable_layer_norm_fusion=False,
    disable_attention=None,
    disable_attention_fusion=False,
    disable_skip_layer_norm=None,
    disable_skip_layer_norm_fusion=False,
    disable_bias_skip_layer_norm=None,
    disable_bias_skip_layer_norm_fusion=False,
    disable_bias_gelu=None,
    disable_bias_gelu_fusion=False,
    disable_embed_layer_norm=True,
    disable_embed_layer_norm_fusion=True,
    enable_gelu_approximation=True,
    use_mask_index=False,
    no_attention_mask=False,
    disable_shape_inference=False,
    use_multi_head_attention=False,
    enable_gemm_fast_gelu_fusion=False,
    use_raw_attention_mask=False,
    disable_group_norm_fusion=True,
    disable_packed_kv=True,
    disable_rotary_embeddings=False
)

Tip:

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

from sentence_transformers import CrossEncoder

# TODO: Fill in the PR number
pr_number = 2
model = CrossEncoder(
    "cross-encoder/stsb-roberta-base",
    revision=f"refs/pr/{pr_number}",
    backend="onnx",
    model_kwargs={"file_name": "model_O3.onnx"},
)

# Verify that everything works as expected
query = "Which planet is known as the Red Planet?"
passages = [
    "Venus is often called Earth's twin because of its similar size and proximity.",
    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    "Jupiter, the largest planet in our solar system, has a prominent red spot.",
    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]

scores = model.predict([(query, passage) for passage in passages])
print(scores)