Instructions to use nlpconnect/roberta-base-squad2-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpconnect/roberta-base-squad2-nq 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="nlpconnect/roberta-base-squad2-nq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nlpconnect/roberta-base-squad2-nq") model = AutoModelForQuestionAnswering.from_pretrained("nlpconnect/roberta-base-squad2-nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from nlpconnect/roberta-base-squad2-nq: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/nlpconnect/roberta-base-squad2-nq/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://nlpconnect/roberta-base-squad2-nq/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/nlpconnect/roberta-base-squad2-nq/resolve/main/flax_model.msgpack
496 MB
- Xet hash:
- c05d3931ee58c5d46fd20dac094f6e8c25e6453d02ace68a30e1e86a68c2a449
- Size of remote file:
- 496 MB
- SHA256:
- 1b9cbfdceea863a3aab039d32442d8c1d7c7ae265fc816b41d1fe4ea5309bdcb
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.