Instructions to use mrp/SCT_Distillation_BERT_Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrp/SCT_Distillation_BERT_Base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrp/SCT_Distillation_BERT_Base") 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_Distillation_BERT_Base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrp/SCT_Distillation_BERT_Base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8794f8da2a62ad6b821eab410dd54088c489271f7a1cc5970aa08bad91e97ad1
- Size of remote file:
- 438 MB
- SHA256:
- a3e53270f337af1f058824582f56e387341fefda00ba326a2b1a16cae83fda96
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.