Instructions to use Palak/albert-large-v2_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Palak/albert-large-v2_squad 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="Palak/albert-large-v2_squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Palak/albert-large-v2_squad") model = AutoModelForQuestionAnswering.from_pretrained("Palak/albert-large-v2_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Palak/albert-large-v2_squad: direct link, hf CLI and curl.
- Browser
- Download file 66.6 MB
-
https://huggingface.co/Palak/albert-large-v2_squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Palak/albert-large-v2_squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Palak/albert-large-v2_squad/resolve/main/pytorch_model.bin
66.6 MB
- Xet hash:
- 813c88cd8632ca67cfdf5745dc24c316334a60e72983e46253a7cb11e4dc4488
- Size of remote file:
- 66.6 MB
- SHA256:
- 46d3d9fb6f230a0ec03ee2c7e0ed21ab15f72d2f085a5c330a8668850b34163a
路
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