Instructions to use ZongqianLi/bert-large-uncased-squad-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZongqianLi/bert-large-uncased-squad-s 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="ZongqianLi/bert-large-uncased-squad-s")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ZongqianLi/bert-large-uncased-squad-s") model = AutoModelForQuestionAnswering.from_pretrained("ZongqianLi/bert-large-uncased-squad-s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ZongqianLi/bert-large-uncased-squad-s: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/ZongqianLi/bert-large-uncased-squad-s/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ZongqianLi/bert-large-uncased-squad-s/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ZongqianLi/bert-large-uncased-squad-s/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- b5e6c9c6b4abd73c36940aab35200d2839e933b4158ebb98aced7c47183e5c34
- Size of remote file:
- 1.34 GB
- SHA256:
- 8ae19ab60f531317a83f5329978c64f6dc57cb2cb8483f38d89a017c1e545d89
路
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