Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:97930
loss:AnglELoss
text-embeddings-inference
Instructions to use CrosswaveOmega/DMRSQ-Utterance-To-Item-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CrosswaveOmega/DMRSQ-Utterance-To-Item-Transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CrosswaveOmega/DMRSQ-Utterance-To-Item-Transformer") sentences = [ "Yes, but online seems to be hit and miss as there are scams out there right now too.", "ITEM 31: In talking about a meaningful, emotionally charged experience, the subject talks in a detached way, as if he or she is not in touch with the feelings that should surround it.", "ITEM 57: The subject distances him or herself from his or her own feelings by speaking about him or herself in the second or third person a lot, as if the subject were talking about someone else.", "ITEM 145: Whenever saying something negative about him or herself, the subject rejects others' attempts to explore positive or more balanced views, and paradoxically becomes even more confirmed in his or her own worthlessness." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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