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 training_args.bin from franklu/pubmed_bert_squadv2: direct link, hf CLI and curl.
- Browser
- Download file 2.74 kB
-
https://huggingface.co/franklu/pubmed_bert_squadv2/resolve/main/training_args.bin
- Command line
-
hf download hf://franklu/pubmed_bert_squadv2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/franklu/pubmed_bert_squadv2/resolve/main/training_args.bin
2.74 kB
- Xet hash:
- db3be5099ce81672045063e17d3a5f9a38c6e3eeb9311cd3199d1a67467017d2
- Size of remote file:
- 2.74 kB
- SHA256:
- 0890f8a5efa1bdca596e7c726964dc8817011914a614ad6704f0500520ac917a
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