Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:23522
loss:SplitHeadContrastiveDistillationLoss
text-embeddings-inference
Instructions to use barealek/peftech-v1-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use barealek/peftech-v1-plus with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("barealek/peftech-v1-plus") sentences = [ "Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals\nQuery: \"Since women say men only think with their dicks do you think she would get offended if I asked her to blow my mind.\" π I hate the people I work with fucking clowns", "Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals\nQuery: /r/ENLIGHTENEDCENTRISM Because someone who wants equality and a nazi are equally as bad, and homophobes have absolutely *no track record* of not letting gays keep practicing their ~~comedy~~ life. As opposed to SJWs who have gone into history responsible for villifying, suppressing and outright killing sexual minorities. But yeah no, middle ground all the way babyyy. You're the smartest guy on Reddit!", "Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals\nQuery: they do not care about me or you, they care about what they can take from you and what they can make you do for them.", "Instruct: Retrieve text with a similar pragmatic profile, including safety, emotion, sentiment, language, and identity-target signals\nQuery: @smkndofpnutdssr @ACLU 70 years ago everyone was brainwashed into being christian and also had coathanger abortions because it was the Great Depression and then thousands on women died because they had unsafe abortions π" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!