Instructions to use franklu/pubmed_bert_squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franklu/pubmed_bert_squadv2 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="franklu/pubmed_bert_squadv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("franklu/pubmed_bert_squadv2") model = AutoModelForQuestionAnswering.from_pretrained("franklu/pubmed_bert_squadv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from franklu/pubmed_bert_squadv2: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/franklu/pubmed_bert_squadv2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://franklu/pubmed_bert_squadv2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/franklu/pubmed_bert_squadv2/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 848135023cf0a6d3c57efd48225ffa88b1858dde7db0b182082d7e2293c852a6
- Size of remote file:
- 436 MB
- SHA256:
- e9c1460c0931f526f21e2de80164d2a7fc741d58f01af7ed4f6ea0e828ac09bd
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.