Instructions to use ktrapeznikov/biobert_v1.1_pubmed_squad_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ktrapeznikov/biobert_v1.1_pubmed_squad_v2 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="ktrapeznikov/biobert_v1.1_pubmed_squad_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ktrapeznikov/biobert_v1.1_pubmed_squad_v2") model = AutoModelForQuestionAnswering.from_pretrained("ktrapeznikov/biobert_v1.1_pubmed_squad_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval.csv from ktrapeznikov/biobert_v1.1_pubmed_squad_v2: direct link, hf CLI and curl.
- Browser
- Download file 315 Bytes
-
https://huggingface.co/ktrapeznikov/biobert_v1.1_pubmed_squad_v2/resolve/main/eval.csv
- Command line
-
hf download hf://ktrapeznikov/biobert_v1.1_pubmed_squad_v2/eval.csv
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curl -L -o eval.csv https://huggingface.co/ktrapeznikov/biobert_v1.1_pubmed_squad_v2/resolve/main/eval.csv
315 Bytes
| ,val | |
| exact,75.97068980038743 | |
| f1,79.37043950121722 | |
| total,11873.0 | |
| HasAns_exact,74.13967611336032 | |
| HasAns_f1,80.94892513460755 | |
| HasAns_total,5928.0 | |
| NoAns_exact,77.79646761984861 | |
| NoAns_f1,77.79646761984861 | |
| NoAns_total,5945.0 | |
| best_exact,75.97068980038743 | |
| best_exact_thresh,0.0 | |
| best_f1,79.37043950121729 | |
| best_f1_thresh,0.0 | |