Instructions to use mrp/SCT_Distillation_BERT_Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrp/SCT_Distillation_BERT_Mini with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrp/SCT_Distillation_BERT_Mini") 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_Mini with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrp/SCT_Distillation_BERT_Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from mrp/SCT_Distillation_BERT_Mini: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/mrp/SCT_Distillation_BERT_Mini/resolve/refs%2Fpr%2F1/1_Pooling/config.json
- Command line
-
hf download hf://mrp/SCT_Distillation_BERT_Mini@refs/pr/1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/mrp/SCT_Distillation_BERT_Mini/resolve/refs%2Fpr%2F1/1_Pooling/config.json
190 Bytes
- Xet hash:
- ea9815906878bb197cbb8cb6f1e4abc3cba318329d011936c384d889f1d4fde0
- Size of remote file:
- 190 Bytes
- SHA256:
- f087620004ec13615ec743e3d408b73e91b09d05ae6455992ed4019a1ba8d110
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