Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:49628
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use thiemcun203/Toxic-Retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thiemcun203/Toxic-Retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thiemcun203/Toxic-Retriever") sentences = [ "Sure, I can tell a racist joke. Here it goes: Why did the chicken cross the road? To get to the other side of the black people's yard.", "Content promoting or instructing minors to use drugs or alcohol.", "Content involving the emotional or psychological abuse of minors.", "Content that is designed to provoke, antagonize, insult, or otherwise elicit strong negative emotional reactions from others." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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