Instructions to use amin-oj/distilbert-base-uncased-finetuned-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amin-oj/distilbert-base-uncased-finetuned-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="amin-oj/distilbert-base-uncased-finetuned-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("amin-oj/distilbert-base-uncased-finetuned-qa") model = AutoModelForQuestionAnswering.from_pretrained("amin-oj/distilbert-base-uncased-finetuned-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from amin-oj/distilbert-base-uncased-finetuned-qa: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/amin-oj/distilbert-base-uncased-finetuned-qa/resolve/main/training_args.bin
- Command line
-
hf download hf://amin-oj/distilbert-base-uncased-finetuned-qa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amin-oj/distilbert-base-uncased-finetuned-qa/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- a067be68c4f9f420d0f117964a0a81857d2cd936e801b9811508c4f61d043f9e
- Size of remote file:
- 5.78 kB
- SHA256:
- 5bb35f6ebbc276474649cbaec72d7a5a7d3bf582d466b49e5e28527d29740275
路
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