Instructions to use microsoft/SportsBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/SportsBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/SportsBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("microsoft/SportsBERT") model = AutoModelForMaskedLM.from_pretrained("microsoft/SportsBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e1c5b45ebba2ba2793602173e20d126d70b00eb81af3b7cba8c38d9828b62e2
- Size of remote file:
- 375 MB
- SHA256:
- 956fa72e34f7d39bc9d72041a749929265d75197ae331ec2d45f4be044c4a031
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.