Instructions to use mbeck/roberta-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbeck/roberta-base-squad2 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="mbeck/roberta-base-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mbeck/roberta-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("mbeck/roberta-base-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mbeck/roberta-base-squad2: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/mbeck/roberta-base-squad2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mbeck/roberta-base-squad2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mbeck/roberta-base-squad2/resolve/main/flax_model.msgpack
496 MB
- Xet hash:
- d67dff6cb22216fe1ddc76ed70e89a0e6a97d7ea8403ecdcccd8eeced0fdbe7c
- Size of remote file:
- 496 MB
- SHA256:
- d2c5d07c1550412226c5560cab9bb9f2e32c136942bcea8f0121ecad84eff3f8
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.