Instructions to use microsoft/prophetnet-large-uncased-squad-qg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/prophetnet-large-uncased-squad-qg with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg") model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/prophetnet-large-uncased-squad-qg: direct link, hf CLI and curl.
- Browser
- Download file 1.57 GB
-
https://huggingface.co/microsoft/prophetnet-large-uncased-squad-qg/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/prophetnet-large-uncased-squad-qg/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/prophetnet-large-uncased-squad-qg/resolve/main/pytorch_model.bin
1.57 GB
- Xet hash:
- 08fdce0a4405f198e4645f707d8883c86bd292c588592e186ec1df8eef779c60
- Size of remote file:
- 1.57 GB
- SHA256:
- 2800b560ddda99227513920a464c00c314c0934e5dccfd0e8a103abcca263e8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.