Instructions to use michaelrglass/bert-large-uncased-sspt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelrglass/bert-large-uncased-sspt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="michaelrglass/bert-large-uncased-sspt", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("michaelrglass/bert-large-uncased-sspt") model = AutoModelForQuestionAnswering.from_pretrained("michaelrglass/bert-large-uncased-sspt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa5a45b5ea3142ff0b5ef5556830200819cec86db5633c9a95ae31eec0005457
- Size of remote file:
- 1.34 GB
- SHA256:
- 65c1b6ac79f3577b1f830441251c630318fec9f5574fb779254490d3c215b23f
路
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