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 pytorch_model.bin from ModelTC/bart-base-squad2: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/ModelTC/bart-base-squad2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ModelTC/bart-base-squad2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ModelTC/bart-base-squad2/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- cb39c85fd5ff8f8ffd02d3331bd8dc8bc65989c016e7b43c29e1a581d007aa64
- Size of remote file:
- 558 MB
- SHA256:
- abf5fd790f02334655e29425ea5c7b28f3de0b9ab250932a9319b9b0357ecf01
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.