Instructions to use kornwtp/ConGen-BERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/ConGen-BERT-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/ConGen-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 kornwtp/ConGen-BERT-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/ConGen-BERT-base") model = AutoModel.from_pretrained("kornwtp/ConGen-BERT-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kornwtp/ConGen-BERT-base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/kornwtp/ConGen-BERT-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kornwtp/ConGen-BERT-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kornwtp/ConGen-BERT-base/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 9576d5968a75a27455f19e94162c7c9a735bd17bda3d39ce0254eb11e2487c9f
- Size of remote file:
- 438 MB
- SHA256:
- cd8dbb755b4bf391203539c51958faa0a3ac74121dc4bb92c8830d46bcb092f5
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