Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
bert
Sentence Transformers
text-embeddings-inference
Instructions to use intfloat/e5-base-unsupervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-base-unsupervised with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-base-unsupervised") 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] - Notebooks
- Google Colab
- Kaggle
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# E5-base-unsupervised
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**This model is similar to [e5-base](https://huggingface.co/intfloat/e5-base) but without supervised fine-tuning.**
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## Limitations
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This model only works for English texts. Long texts will be truncated to at most 512 tokens.
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pipeline_tag: sentence-similarity
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# E5-base-unsupervised
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**This model is similar to [e5-base](https://huggingface.co/intfloat/e5-base) but without supervised fine-tuning.**
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## Limitations
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This model only works for English texts. Long texts will be truncated to at most 512 tokens.
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