Instructions to use danielsaggau/scotus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danielsaggau/scotus with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danielsaggau/scotus") 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 danielsaggau/scotus with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danielsaggau/scotus") model = AutoModel.from_pretrained("danielsaggau/scotus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cce627068ea71f7ff214423ceee5cf50590275d8899404fc478af63af9ff8c38
- Size of remote file:
- 167 MB
- SHA256:
- 9042c7be143177652a608ed03b72b97ed34e0c61322804bde23814a7bd23f281
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