Instructions to use michaelrglass/bert-base-uncased-sspt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelrglass/bert-base-uncased-sspt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="michaelrglass/bert-base-uncased-sspt")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("michaelrglass/bert-base-uncased-sspt") model = AutoModelForQuestionAnswering.from_pretrained("michaelrglass/bert-base-uncased-sspt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0129a440f2a9f82649c51b562be1e3ae355fc4fea118e6b095449ca3d61a94ab
- Size of remote file:
- 436 MB
- SHA256:
- 5d8a73a45d8ed9aa4082572cb1f6a8fbb32f05845ebed6dfcaa121f0487d3db4
路
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