Instructions to use pranav-s/MaterialsBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pranav-s/MaterialsBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pranav-s/MaterialsBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pranav-s/MaterialsBERT") model = AutoModelForMaskedLM.from_pretrained("pranav-s/MaterialsBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 673a51921f1b4c93e915ad61b0be8dbfee68ef0a808d86c68c8f9efda13632fd
- Size of remote file:
- 438 MB
- SHA256:
- 34d6035e704a81360e8eef77c2b9cf775a348f0825c1ff20a60ee653d62429de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.