Instructions to use mrp/SCT_BERT_Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrp/SCT_BERT_Small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrp/SCT_BERT_Small") 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_Small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrp/SCT_BERT_Small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from mrp/SCT_BERT_Small: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/mrp/SCT_BERT_Small/resolve/refs%2Fpr%2F1/1_Pooling/config.json
- Command line
-
hf download hf://mrp/SCT_BERT_Small@refs/pr/1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/mrp/SCT_BERT_Small/resolve/refs%2Fpr%2F1/1_Pooling/config.json
190 Bytes
- Xet hash:
- da5c31fcb82d5791cb5ec4c91bb5d59f64bc154494950657af54a7876f325bcc
- Size of remote file:
- 190 Bytes
- SHA256:
- 87bc2276966306a852eeb365e10008072b05aacc5540741208d959c02d1fc669
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