Instructions to use clagator/biobert_squad2_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clagator/biobert_squad2_cased 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="clagator/biobert_squad2_cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("clagator/biobert_squad2_cased") model = AutoModelForQuestionAnswering.from_pretrained("clagator/biobert_squad2_cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from clagator/biobert_squad2_cased: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/clagator/biobert_squad2_cased/resolve/main/training_args.bin
- Command line
-
hf download hf://clagator/biobert_squad2_cased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/clagator/biobert_squad2_cased/resolve/main/training_args.bin
1.57 kB
- Xet hash:
- 9f2c5ac75446403a5bb5ea2835a280ac79523aa4e8871b3329f0f1da0fce4eba
- Size of remote file:
- 1.57 kB
- SHA256:
- c6cb3fb217d6993d8ddd268a0944ac381f08ce43765bc3914c075ada4ef6061f
路
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