Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:19985
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use dwulff/mpnet-cocs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dwulff/mpnet-cocs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dwulff/mpnet-cocs") sentences = [ "A trigger of contamination OCD: own hands", "A trigger of contamination OCD: parking lot buttons", "A trigger of contamination OCD: touched by strangers", "A trigger of contamination OCD: using public toilets" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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README.md
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This is a [sentence-transformers](https://www.SBERT.net) model that generates 768-dimensional semantic vectors of triggers of contamination obsessive compulsive symptoms (C-OCS).
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The base model ([all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)) has been fine-tuned on
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See PREPRINT for details.
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This is a [sentence-transformers](https://www.SBERT.net) model that generates 768-dimensional semantic vectors of triggers of contamination obsessive compulsive symptoms (C-OCS).
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The base model ([all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)) has been fine-tuned on 20k pairs of C-OCS triggers rated for similarity by [Llama-3.3-70b-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).
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See PREPRINT for details.
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