Instructions to use hungphongtrn/videberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hungphongtrn/videberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hungphongtrn/videberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hungphongtrn/videberta-base") model = AutoModelForTokenClassification.from_pretrained("hungphongtrn/videberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hungphongtrn/videberta-base: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/hungphongtrn/videberta-base/resolve/main/training_args.bin
- Command line
-
hf download hf://hungphongtrn/videberta-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hungphongtrn/videberta-base/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- ccaa819bf588e91a5f2f12b6a1fdae7b42e34d08dc74bf0cd82fe13a8142b5bf
- Size of remote file:
- 4.47 kB
- SHA256:
- 2af6ea6d4d57e4793e3f886b60336e8d26ae99e1897974301996c3095f430da3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.