Instructions to use brao/innox-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brao/innox-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brao/innox-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brao/innox-bert") model = AutoModelForTokenClassification.from_pretrained("brao/innox-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from brao/innox-bert: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/brao/innox-bert/resolve/main/training_args.bin
- Command line
-
hf download hf://brao/innox-bert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/brao/innox-bert/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 0dc8448a4ae542de3097c325cacdb4c9fb253f1ed67cb9722a8357c3dd0ccc8f
- Size of remote file:
- 3.38 kB
- SHA256:
- fbe7cdcd926dae7c2318f04bcf681a1ab9fa3c0d3736d6d2968248c5934cfc06
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.