Instructions to use javauser/nda_central_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use javauser/nda_central_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="javauser/nda_central_learning")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("javauser/nda_central_learning") model = AutoModelForSequenceClassification.from_pretrained("javauser/nda_central_learning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0708eeddc42711dfda07a49cec532f10819f3e9e9213866161dbb54307ebf182
- Size of remote file:
- 268 MB
- SHA256:
- 6936bf4788baea3317e141a9e0ef0cf936bf64a6ed92b02dccf02dd2ff81bb71
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.