Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
fitness
retrieval
text-embeddings-inference
Instructions to use OrDora/coachtwin-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OrDora/coachtwin-embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OrDora/coachtwin-embedder") 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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## Limitations
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Base checkpoint, **not fine-tuned**. Evaluated only on synthetic
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English workout descriptions. Not fitness or medical advice.
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## Limitations
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Base checkpoint, **not fine-tuned**. Evaluated only on synthetic
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English workout descriptions. Not fitness or medical advice.
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