Instructions to use priyaganesh2050/bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use priyaganesh2050/bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="priyaganesh2050/bert-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("priyaganesh2050/bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from priyaganesh2050/bert-tiny: direct link, hf CLI and curl.
- Browser
- Download file 17.8 MB
-
https://huggingface.co/priyaganesh2050/bert-tiny/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://priyaganesh2050/bert-tiny/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/priyaganesh2050/bert-tiny/resolve/main/pytorch_model.bin
17.8 MB
- Xet hash:
- 0ec0a64f22848486e2f2ece33b16f88388790e5d2c6b0b825550e5afc9940eaa
- Size of remote file:
- 17.8 MB
- SHA256:
- dab2c2bddcfb48ea430ef63fd76d46d67d704487844d967256a50dd7d7fd0a66
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.