Instructions to use McGill-NLP/bart-qg-mlquestions-backtraining with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/bart-qg-mlquestions-backtraining with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/bart-qg-mlquestions-backtraining") model = AutoModelForSeq2SeqLM.from_pretrained("McGill-NLP/bart-qg-mlquestions-backtraining", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8bf321bb0a93f3cc5c1bf9b6c267112d9a541022f5bd4a8e95024ca4ced0f69
- Size of remote file:
- 558 MB
- SHA256:
- 66b8ca027e932bba0ff3bca994d92d2641946c9594c2e68ede4f205e56acc51b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.