Instructions to use pbeyens/RandomCropped-repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pbeyens/RandomCropped-repo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pbeyens/RandomCropped-repo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pbeyens/RandomCropped-repo") model = AutoModelForTokenClassification.from_pretrained("pbeyens/RandomCropped-repo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pbeyens/RandomCropped-repo: direct link, hf CLI and curl.
- Browser
- Download file 521 MB
-
https://huggingface.co/pbeyens/RandomCropped-repo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pbeyens/RandomCropped-repo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pbeyens/RandomCropped-repo/resolve/main/pytorch_model.bin
521 MB
- Xet hash:
- e2ab3595ff8d45cf5b6bc80bdcb2848309d5b4685fd2915ac510039e8b9c3131
- Size of remote file:
- 521 MB
- SHA256:
- 3ec22adbb128d19eb75b074d9f24084e6d455fbfccf5eac22924f080b563e783
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.