Instructions to use deepset/roberta-large-squad2-hp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/roberta-large-squad2-hp with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="deepset/roberta-large-squad2-hp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-large-squad2-hp") model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-large-squad2-hp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from deepset/roberta-large-squad2-hp: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/deepset/roberta-large-squad2-hp/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://deepset/roberta-large-squad2-hp/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/deepset/roberta-large-squad2-hp/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- d2f60dd44ae24f1813d6da8febc2d51fa5af55772d08ce7dd19ab66d25666fc7
- Size of remote file:
- 1.42 GB
- SHA256:
- 832639a5ed29ed20c659a5ff29626beffa1516c75854e5bc54909b5370c5fd3c
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.