Instructions to use mateiaass/albert-base-qa-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaass/albert-base-qa-2 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/albert-base-qa-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mateiaass/albert-base-qa-2") model = AutoModelForQuestionAnswering.from_pretrained("mateiaass/albert-base-qa-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mateiaass/albert-base-qa-2: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/mateiaass/albert-base-qa-2/resolve/main/training_args.bin
- Command line
-
hf download hf://mateiaass/albert-base-qa-2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mateiaass/albert-base-qa-2/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- eb17e618f7b18ee2713db57cda9e38c6bc5846c99950867393ada4d1472d349c
- Size of remote file:
- 4.54 kB
- SHA256:
- 524c32994bc2d176f290666cabbaf0bf49a44d39fd2fd6f3baf43b6c0ac3f5fb
路
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