Instructions to use danielsaggau/bregman_base_ecthr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danielsaggau/bregman_base_ecthr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danielsaggau/bregman_base_ecthr") 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/bregman_base_ecthr with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danielsaggau/bregman_base_ecthr") model = AutoModel.from_pretrained("danielsaggau/bregman_base_ecthr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f37db226cfb22efc6a0ff68e01a6eb8c14d7ca660e4ae8a35b46bdcfd946769a
- Size of remote file:
- 167 MB
- SHA256:
- dab32530fc2e7d8c8b12beb959bc2db4ff62211d2203880e6aa19efa6787cdf4
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