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 training_args.bin from RameshArvind/roberta_long_answer_nq: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/RameshArvind/roberta_long_answer_nq/resolve/main/training_args.bin
- Command line
-
hf download hf://RameshArvind/roberta_long_answer_nq/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RameshArvind/roberta_long_answer_nq/resolve/main/training_args.bin
2.03 kB
- Xet hash:
- 2efc9fc277ed41c61e3256455641af0c36f8179e5d7151a8f2c9b95c38af7db1
- Size of remote file:
- 2.03 kB
- SHA256:
- 5601974d823b5d120c9e871d50a352c0dfaf539fc3ebafe4bf8f3f2f044bc32b
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