Instructions to use EricPeter/distilbert-base-uncased-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricPeter/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="EricPeter/distilbert-base-uncased-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("EricPeter/distilbert-base-uncased-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("EricPeter/distilbert-base-uncased-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EricPeter/distilbert-base-uncased-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/EricPeter/distilbert-base-uncased-finetuned-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EricPeter/distilbert-base-uncased-finetuned-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EricPeter/distilbert-base-uncased-finetuned-squad/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 09be7d65931f67161eef584f4ad89087b0dafa6b5e6e209cb6b4984448bf1c49
- Size of remote file:
- 265 MB
- SHA256:
- 31d17eb23d7c2864ab161d1acacf9dac54d7d0aa847b5d261f93078829a28d31
路
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