Instructions to use Shubham09/bartlatest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubham09/bartlatest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Shubham09/bartlatest")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Shubham09/bartlatest") model = AutoModelForQuestionAnswering.from_pretrained("Shubham09/bartlatest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 299fac5d073ff11b0bdd11ddd33c0dcb8a1031769df35919912dbb90298cf130
- Size of remote file:
- 3.58 kB
- SHA256:
- 123b9baa1e57f7017677e23b03c0a587f5968e5b60fb0427b20244f9631d4e74
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.