Instructions to use lordtt13/COVID-SciBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lordtt13/COVID-SciBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lordtt13/COVID-SciBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lordtt13/COVID-SciBERT") model = AutoModelForMaskedLM.from_pretrained("lordtt13/COVID-SciBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82caa5a89ec273a4e95994c4462d8ebe9b6e3ebb7759d7ba8e9f47e3b9998996
- Size of remote file:
- 445 MB
- SHA256:
- 5231637f9e96af108483d959dd53a39bcfb360bcfc99c74c4bc7224f7e8f2579
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.