Instructions to use nirupamadasari/bartlfqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nirupamadasari/bartlfqa 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="nirupamadasari/bartlfqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nirupamadasari/bartlfqa") model = AutoModelForQuestionAnswering.from_pretrained("nirupamadasari/bartlfqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nirupamadasari/bartlfqa: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/nirupamadasari/bartlfqa/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://nirupamadasari/bartlfqa@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nirupamadasari/bartlfqa/resolve/refs%2Fpr%2F1/training_args.bin
3.9 kB
- Xet hash:
- 6985290615a988fb6043c81a17e272516ed6d329ebb84d1baf92f433264a3d64
- Size of remote file:
- 3.9 kB
- SHA256:
- 43c72751ed823f7ca8119d87fa8e2d18236818f30cabcd0207bada7907eedfce
路
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