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 openvino model 'openvino_model_qint8_quantized.xml'
Verified signatureHello! *This pull request has been automatically generated from the [`export_static_quantized_openvino_model`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.backend.export_static_quantized_openvino_model) function from the Sentence Transformers library.* ## Config ```python OVQuantizationConfig( quant_method=<OVQuantizationMethod.DEFAULT: 'default'> ) ``` ## Tip: Consider testing this pull request before merging by loading the model from this PR with the `revision` argument: ```python from sentence_transformers import SentenceTransformer # TODO: Fill in the PR number pr_number = 2 model = SentenceTransformer( "intfloat/e5-base-v2", revision=f"refs/pr/{pr_number}", backend="openvino", model_kwargs={"file_name": "openvino_model_qint8_quantized.xml"}, ) # 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) similari…c0aba6a