Instructions to use laiking/biomedbert-outcomes-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laiking/biomedbert-outcomes-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="laiking/biomedbert-outcomes-ner")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("laiking/biomedbert-outcomes-ner") model = AutoModel.from_pretrained("laiking/biomedbert-outcomes-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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language:
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- en
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pipeline_tag: token-classification
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tags:
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- token-classification
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---
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PubMedBERT-base model finetuned for token classification of primary outcomes and secondary outcomes in clinical trials scientific articles sentences.
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Trained on A. Koroleva dataset of sentences extracted from clinical trials articles.
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