Sentence Similarity
sentence-transformers
Safetensors
modernbert
semantic-similarity
representlm
text-embeddings-inference
Instructions to use suproteem/RepresentLM-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use suproteem/RepresentLM-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("suproteem/RepresentLM-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] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# RepresentLM-v2
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This is a sentence-transformers model: It maps sentences and paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search.
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---
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- semantic-similarity
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- representlm
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base_model: suproteem/StoriesLM-v2-1979
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---
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# RepresentLM-v2
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This is a sentence-transformers model: It maps sentences and paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search.
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