Instructions to use innocent-charles/Swahili-question-answer-latest-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use innocent-charles/Swahili-question-answer-latest-cased 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="innocent-charles/Swahili-question-answer-latest-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("innocent-charles/Swahili-question-answer-latest-cased") model = AutoModelForQuestionAnswering.from_pretrained("innocent-charles/Swahili-question-answer-latest-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from innocent-charles/Swahili-question-answer-latest-cased: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/innocent-charles/Swahili-question-answer-latest-cased/resolve/main/training_args.bin
- Command line
-
hf download hf://innocent-charles/Swahili-question-answer-latest-cased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/innocent-charles/Swahili-question-answer-latest-cased/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 13980a7a9dd776a3c6c85135c2a87ea7e7e39d242f832ef3079439f6c0d08fc4
- Size of remote file:
- 2.99 kB
- SHA256:
- 26bf64e08288d7cc4b9e77d12f9b43ebd8567a759200fa4162f31d20f661ee34
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