Instructions to use lfoppiano/MatTPUSciBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lfoppiano/MatTPUSciBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lfoppiano/MatTPUSciBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lfoppiano/MatTPUSciBERT") model = AutoModelForMaskedLM.from_pretrained("lfoppiano/MatTPUSciBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6d840dfac1caf41e60caeca1859fa15248e9269ccab92747590f543d60bab05
- Size of remote file:
- 442 MB
- SHA256:
- 5c816b4ff4c9b8c929e1eef128b424ab4f64da2917d4b0ac3485921373ba43d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.