Instructions to use valhalla/longformer-base-4096-finetuned-squadv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use valhalla/longformer-base-4096-finetuned-squadv1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="valhalla/longformer-base-4096-finetuned-squadv1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1") model = AutoModelForQuestionAnswering.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25c2fd21d99ad2367b073c3940b30d800dd5aa5e986e17d5379f465381b6f424
- Size of remote file:
- 595 MB
- SHA256:
- 862e538b43ec2fdd1031390577a6aa24a480c19aeb9a326e20393da5c330bf77
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.