Instructions to use ydshieh/tiny-random-BertForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-BertForQuestionAnswering 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="ydshieh/tiny-random-BertForQuestionAnswering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-BertForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("ydshieh/tiny-random-BertForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ydshieh/tiny-random-BertForQuestionAnswering: direct link, hf CLI and curl.
- Browser
- Download file 379 kB
-
https://huggingface.co/ydshieh/tiny-random-BertForQuestionAnswering/resolve/refs%2Fpr%2F4/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-BertForQuestionAnswering@refs/pr/4/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-BertForQuestionAnswering/resolve/refs%2Fpr%2F4/pytorch_model.bin
379 kB
- Xet hash:
- d2b94d5009f232925450828420e8a60888ddb8f50c4a47052cf278fbb91933e5
- Size of remote file:
- 379 kB
- SHA256:
- f3b44d302d5f86677c1dbd89fb3f8857aba805d5064a5bcef3552e290bc1cb4e
路
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