Instructions to use Shubham09/bart_lfqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubham09/bart_lfqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Shubham09/bart_lfqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Shubham09/bart_lfqa") model = AutoModelForQuestionAnswering.from_pretrained("Shubham09/bart_lfqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd370f9227d0b421035c40c1d621dbffd0e38f19571a7eb30fbb6b69d0156a84
- Size of remote file:
- 1.63 GB
- SHA256:
- 04d0a084aa810bccd0c8fcce758a48f7958be770203371da0b81fe32dd0cee67
路
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