Instructions to use kornwtp/ConGen-model-wangchanberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/ConGen-model-wangchanberta with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/ConGen-model-wangchanberta") 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-model-wangchanberta with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kornwtp/ConGen-model-wangchanberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bc396eb83a6ac986e6502f09cc1ac6ce1ed1260f65561b10b2a277f3ca7b6ce
- Size of remote file:
- 2.36 MB
- SHA256:
- 610f899271d5c5af6f88ae11be17e4c46358ea58f50e7b57cd36004483f7ddc6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.