Instructions to use mateiaass/albertbase-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaass/albertbase-qa 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="mateiaass/albertbase-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mateiaass/albertbase-qa") model = AutoModelForQuestionAnswering.from_pretrained("mateiaass/albertbase-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mateiaass/albertbase-qa: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/mateiaass/albertbase-qa/resolve/main/training_args.bin
- Command line
-
hf download hf://mateiaass/albertbase-qa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mateiaass/albertbase-qa/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 8de9df9c16007d93157e593b94ff0e5c65e1d6a4bbb3e19a9320ba5d11b1879d
- Size of remote file:
- 4.03 kB
- SHA256:
- 7a3974844f76e3c12399a79069d40dcea89519487e5384d9de8016119d774342
路
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