Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use SetFit/MiniLM_L3_clinc_oos_plus_distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SetFit/MiniLM_L3_clinc_oos_plus_distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SetFit/MiniLM_L3_clinc_oos_plus_distilled") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use SetFit/MiniLM_L3_clinc_oos_plus_distilled with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SetFit/MiniLM_L3_clinc_oos_plus_distilled") model = AutoModel.from_pretrained("SetFit/MiniLM_L3_clinc_oos_plus_distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80e1fda60bc19270443924fd89c201a61f5e74c4b3692f850246addec2bc045f
- Size of remote file:
- 69.6 MB
- SHA256:
- 883a987e64e51311dc5cd01d0fc9788c0a1d163e6ec59ad49ce535920f90da05
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