Instructions to use msms/distilbert-base-uncased-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msms/distilbert-base-uncased-finetuned-squad 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="msms/distilbert-base-uncased-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("msms/distilbert-base-uncased-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("msms/distilbert-base-uncased-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from msms/distilbert-base-uncased-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/msms/distilbert-base-uncased-finetuned-squad/resolve/main/training_args.bin
- Command line
-
hf download hf://msms/distilbert-base-uncased-finetuned-squad/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/msms/distilbert-base-uncased-finetuned-squad/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 82e1c2ef7d82e8f461aac8eb45b48dc76827aae7a6479a9b49662e2ad6b386e0
- Size of remote file:
- 3.31 kB
- SHA256:
- 77750d522590aab3be970836057182a46d963e6ba4d30b0f1d05818cbd98e829
路
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