Instructions to use gszabo/base_webcorpus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gszabo/base_webcorpus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gszabo/base_webcorpus")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gszabo/base_webcorpus") model = AutoModelForMaskedLM.from_pretrained("gszabo/base_webcorpus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gszabo/base_webcorpus: direct link, hf CLI and curl.
- Browser
- Download file 413 MB
-
https://huggingface.co/gszabo/base_webcorpus/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gszabo/base_webcorpus/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gszabo/base_webcorpus/resolve/main/pytorch_model.bin
413 MB
- Xet hash:
- a3d2bd40bc55934f73b9773ff223568412e862b97865f0257523e264b0a79687
- Size of remote file:
- 413 MB
- SHA256:
- 5f15de8a2989d683029f2d78efe2e047015f50f1d34434e32649d5b3f9901a2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.