Instructions to use dewdev/question_and_answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dewdev/question_and_answer 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="dewdev/question_and_answer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dewdev/question_and_answer") model = AutoModelForQuestionAnswering.from_pretrained("dewdev/question_and_answer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dewdev/question_and_answer: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
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https://huggingface.co/dewdev/question_and_answer/resolve/main/README.md
- Command line
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hf download hf://dewdev/question_and_answer/README.md
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curl -L -o README.md https://huggingface.co/dewdev/question_and_answer/resolve/main/README.md
261 Bytes
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert/distilbert-base-cased-distilled-squad | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: Docty/question_and_answer | |
| results: [] | |
| this is a clone of Docty/question_and_answer | |