Instructions to use helloNet/public_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helloNet/public_models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="helloNet/public_models")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("helloNet/public_models") model = AutoModelForMaskedLM.from_pretrained("helloNet/public_models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4898503b726bb460253c7b53c0821f7b2dd8e92752a7cb6d149a2c1a72e12ed
- Size of remote file:
- 438 MB
- SHA256:
- caba1cd287b757a27acbb24da1af5f805e562285e56333b97b25e58cd2bb24b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.