Instructions to use kornwtp/ConGen-BERT-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/ConGen-BERT-Small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/ConGen-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 kornwtp/ConGen-BERT-Small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/ConGen-BERT-Small") model = AutoModel.from_pretrained("kornwtp/ConGen-BERT-Small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kornwtp/ConGen-BERT-Small: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/kornwtp/ConGen-BERT-Small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kornwtp/ConGen-BERT-Small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kornwtp/ConGen-BERT-Small/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 9bc9054db8ef96bed1835ad26bc1027b00fc36a211c4dde3159bee742aa2f29d
- Size of remote file:
- 115 MB
- SHA256:
- f176bb0e76559b9913282008c564669641d408ba9d9585c73d3b99e53b8bc33f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.