Instructions to use SRDdev/QuAC-QA-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/QuAC-QA-BERT 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="SRDdev/QuAC-QA-BERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SRDdev/QuAC-QA-BERT") model = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QuAC-QA-BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SRDdev/QuAC-QA-BERT: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/SRDdev/QuAC-QA-BERT/resolve/main/training_args.bin
- Command line
-
hf download hf://SRDdev/QuAC-QA-BERT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SRDdev/QuAC-QA-BERT/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- ed7bc6ed8198d6b17a6caf67f6f4ef9679ace39a314f7a682868226cea1e803f
- Size of remote file:
- 3.58 kB
- SHA256:
- acdc32753c122f153a9de6b5dd891ad72628fd33f0971548d9a6f1796f99c0bb
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.