Instructions to use buvata/bertTitle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buvata/bertTitle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="buvata/bertTitle")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("buvata/bertTitle") model = AutoModelForMaskedLM.from_pretrained("buvata/bertTitle", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from buvata/bertTitle: direct link, hf CLI and curl.
- Browser
- Download file 540 MB
-
https://huggingface.co/buvata/bertTitle/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://buvata/bertTitle/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/buvata/bertTitle/resolve/main/pytorch_model.bin
540 MB
- Xet hash:
- 3fc1176a15afa95eba3885cead0bd73d84f99c25544be7f2660aa70fea66c1ab
- Size of remote file:
- 540 MB
- SHA256:
- 7c2606505a781d69c058e3984e7738464a10ed1b702392278dcf99e7780a5b1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.