Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
datadreamer
datadreamer-0.46.0
Synthetic
feature-extraction
text-embeddings-inference
Instructions to use fineinstructions/instruction_template_retrieval_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fineinstructions/instruction_template_retrieval_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fineinstructions/instruction_template_retrieval_embedding") 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
Add autocitation section
Browse files
README.md
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@@ -136,12 +136,14 @@ This model was trained with a synthetic dataset with [DataDreamer 🤖💤](http
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If you use this project in your research please cite:
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@article{
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If you use this project in your research please cite:
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@article{patel2026fineinstructions,
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title={FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale},
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author={Patel, Ajay and Raffel, Colin and Callison-Burch, Chris},
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journal={arXiv preprint arXiv:2601.22146},
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year={2026},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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doi={10.48550/arXiv.2601.22146}
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}
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```
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