Instructions to use rsml/bbert_qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rsml/bbert_qa 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="rsml/bbert_qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rsml/bbert_qa") model = AutoModelForQuestionAnswering.from_pretrained("rsml/bbert_qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rsml/bbert_qa: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/rsml/bbert_qa/resolve/main/training_args.bin
- Command line
-
hf download hf://rsml/bbert_qa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rsml/bbert_qa/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- b518819c10678e994b3545e44e9fce929fccdba19d1f61670d7ba4f070878e76
- Size of remote file:
- 3.9 kB
- SHA256:
- a574ff1b13a26641b2c8476c6b4d3d93aef3e2fe441fc1b8bcfb5007c5c6b120
路
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