Instructions to use AllenGeng/OCamlBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AllenGeng/OCamlBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AllenGeng/OCamlBert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AllenGeng/OCamlBert") model = AutoModelForMaskedLM.from_pretrained("AllenGeng/OCamlBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AllenGeng/OCamlBert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/AllenGeng/OCamlBert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AllenGeng/OCamlBert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AllenGeng/OCamlBert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c6bf0bf9dad998ea22d4d90ac970a7ab8645c3f7e56928093f71f1c851c6b08d
- Size of remote file:
- 438 MB
- SHA256:
- e766981504f368e2a03b9815c899b2b3f7b5fb4a605c127100717be5bc0b4d73
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.