Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/e5-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-base-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add exported onnx model 'model_O4.onnx'
Verified signatureHello! *This pull request has been automatically generated from the [`export_optimized_onnx_model`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.backend.export_optimized_onnx_model) function from the Sentence Transformers library.* ## Config ```python OptimizationConfig( optimization_level=2, optimize_for_gpu=True, fp16=True, 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…74f8dcf
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Adding ONNX file of this model (#11)
- Adding ONNX file of this model (3a233be0ddddb328f8b63b9f9b65b3969ba2f504) Co-authored-by: Nayan Shah <nayan06@users.noreply.huggingface.co>1c644c9