Instructions to use mrp/SCT_BERT_Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrp/SCT_BERT_Large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrp/SCT_BERT_Large") 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 mrp/SCT_BERT_Large with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrp/SCT_BERT_Large", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1296b2779a36557dde13b55ceec6a98e60092dd3f382d1c72bf0246ff2279cb5
- Size of remote file:
- 123 Bytes
- SHA256:
- 7f17151000092ad1fc3c90507a35fe586b11582b2890f361dfc3dde3b4f45cf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.