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
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# kornwtp/ConGen-BERT-Small
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This is a [SCT](https://github.com/mrpeerat/SCT) model: It maps sentences to a dense vector space and can be used for tasks like semantic search.
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This is a [SCT](https://github.com/mrpeerat/SCT) model: It maps sentences to a dense vector space and can be used for tasks like semantic search.
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