Instructions to use mrp/SCT_Distillation_BERT_Tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrp/SCT_Distillation_BERT_Tiny with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrp/SCT_Distillation_BERT_Tiny") 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_Tiny with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrp/SCT_Distillation_BERT_Tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from mrp/SCT_Distillation_BERT_Tiny: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/mrp/SCT_Distillation_BERT_Tiny/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://mrp/SCT_Distillation_BERT_Tiny/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/mrp/SCT_Distillation_BERT_Tiny/resolve/main/1_Pooling/config.json
190 Bytes
- Xet hash:
- ce11aeb21ddeb47f574c2639b5a3225ac26d0d80448531a9679d14305cbdd752
- Size of remote file:
- 190 Bytes
- SHA256:
- 78e1e6b49c4be1e31644a308c4dc02cb7847d0fbf1383373cf18a6491afd1c02
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