Instructions to use haritzpuerto/distilbert-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haritzpuerto/distilbert-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="haritzpuerto/distilbert-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("haritzpuerto/distilbert-squad") model = AutoModelForQuestionAnswering.from_pretrained("haritzpuerto/distilbert-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from haritzpuerto/distilbert-squad: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/haritzpuerto/distilbert-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://haritzpuerto/distilbert-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/haritzpuerto/distilbert-squad/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- d2718c22c92b11b96381a9741c6c9ece94758c529385c35eae426a3899dff7bc
- Size of remote file:
- 265 MB
- SHA256:
- 98366cb0b6dfe3e26bae2945ed3f384cb4713058e96a6749d11a2ad2c0df261a
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