Instructions to use RameshArvind/roberta_long_answer_nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RameshArvind/roberta_long_answer_nq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RameshArvind/roberta_long_answer_nq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RameshArvind/roberta_long_answer_nq") model = AutoModelForSequenceClassification.from_pretrained("RameshArvind/roberta_long_answer_nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from RameshArvind/roberta_long_answer_nq: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/RameshArvind/roberta_long_answer_nq/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://RameshArvind/roberta_long_answer_nq/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/RameshArvind/roberta_long_answer_nq/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 6bd6bc92243b22319f4879a192da7540b74ceefb6607ae1b95a15da123533ade
- Size of remote file:
- 499 MB
- SHA256:
- bb90802a0a69e5a3fe7ea024a9b98c770c71248dea4e6142877602cdf167e573
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