Instructions to use Kate-lf/rag-qa-base-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kate-lf/rag-qa-base-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="Kate-lf/rag-qa-base-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Kate-lf/rag-qa-base-bert") model = AutoModelForQuestionAnswering.from_pretrained("Kate-lf/rag-qa-base-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kate-lf/rag-qa-base-bert: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/Kate-lf/rag-qa-base-bert/resolve/main/training_args.bin
- Command line
-
hf download hf://Kate-lf/rag-qa-base-bert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kate-lf/rag-qa-base-bert/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- e37b3f7b2ca94cd9e0b65fdf60fd04d83018c783b8efb7102c102d99d3072be9
- Size of remote file:
- 5.78 kB
- SHA256:
- adb5622744e02f39001e55ea363f2a99bdc7f609ba7f8153faec66a5aceecb44
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