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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrp/SCT_BERT_Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vocab.txt from mrp/SCT_BERT_Large: direct link, hf CLI and curl.
- Browser
- Download file 232 kB
-
https://huggingface.co/mrp/SCT_BERT_Large/resolve/main/vocab.txt
- Command line
-
hf download hf://mrp/SCT_BERT_Large/vocab.txt
-
curl -L -o vocab.txt https://huggingface.co/mrp/SCT_BERT_Large/resolve/main/vocab.txt
232 kB
- Xet hash:
- 23d0b8a3359f1ce8a443e54b4d4d050dd6788dba0d449e5d0e1a21d86c2f0140
- Size of remote file:
- 232 kB
- SHA256:
- 07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3
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