Instructions to use ModelTC/bart-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-squad2 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="ModelTC/bart-base-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("ModelTC/bart-base-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ModelTC/bart-base-squad2: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/ModelTC/bart-base-squad2/resolve/main/optimizer.pt
- Command line
-
hf download hf://ModelTC/bart-base-squad2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ModelTC/bart-base-squad2/resolve/main/optimizer.pt
1.12 GB
- Xet hash:
- b7cd2f821422c6dfefcf381ee92b230c7f10845a4556b628fade926a3c2d5650
- Size of remote file:
- 1.12 GB
- SHA256:
- ece5333e6bf0883afd48d2c77a43bce7c381048f0a320e3d0eb52d47ff7dea49
路
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