Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:8386
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use dalek-ai/multilingual-next-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dalek-ai/multilingual-next-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dalek-ai/multilingual-next-v1") sentences = [ "You are Mei Davis in 80217. You want to return the water bottle, and exchange the pet bed and office chair to the cheapest version. Mention the two things together. If you can only do one of the two things, you prefer to do whatever saves you most money, but you want to know the money you can save in both ways. You are in debt and sad today, but very brief.", "Transfer the user to a human agent, with a summary of the user's issue. Only transfer if the user explicitly asks for a human agent, or if the user's issue cannot be resolved by the agent with the available tools.", "Return some items of a delivered order. The order status will be changed to 'return requested'. The agent needs to explain the return detail and ask for explicit user confirmation (yes/no) to proceed. The user will receive follow-up email for how and where to return the item.", "Allows users to donate money for animal welfare" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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