Instructions to use cwtpc/wangchanberta-ner-8989 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cwtpc/wangchanberta-ner-8989 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cwtpc/wangchanberta-ner-8989")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cwtpc/wangchanberta-ner-8989") model = AutoModelForTokenClassification.from_pretrained("cwtpc/wangchanberta-ner-8989", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cwtpc/wangchanberta-ner-8989: direct link, hf CLI and curl.
- Browser
- Download file 419 MB
-
https://huggingface.co/cwtpc/wangchanberta-ner-8989/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cwtpc/wangchanberta-ner-8989/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cwtpc/wangchanberta-ner-8989/resolve/main/pytorch_model.bin
419 MB
- Xet hash:
- 17da96e71bd7cf2ee177be4c90a3f839b70146c2ca87d5cfb08a221c2b6c336b
- Size of remote file:
- 419 MB
- SHA256:
- a53e72cb43480c9d1027f20bc1e10474a46bd7b8cc4c053deb0e23b17ddf38c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.