Instructions to use sagteam/pharm-relation-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagteam/pharm-relation-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sagteam/pharm-relation-extraction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sagteam/pharm-relation-extraction") model = AutoModelForSequenceClassification.from_pretrained("sagteam/pharm-relation-extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -25,4 +25,17 @@ Here are the accuracy, estimated by the f1 score metric for the recognition of r
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| ADR–Drugname | Drugname–Diseasename | Drugname–SourceInfoDrug | Diseasename–Indication |
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| ------------- | -------------------- | ----------------------- | ---------------------- |
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| 0.955 | 0.892 | 0.922 | 0.891 |
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| ADR–Drugname | Drugname–Diseasename | Drugname–SourceInfoDrug | Diseasename–Indication |
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| ------------- | -------------------- | ----------------------- | ---------------------- |
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| 0.955 | 0.892 | 0.922 | 0.891 |
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Citation info
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----
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If you have found our results helpful in your work, feel free to cite our publication as:
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'''
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@article{sboev2021extraction,
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title={Extraction of the Relations between Significant Pharmacological Entities in Russian-Language Internet Reviews on Medications},
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author={Sboev, Alexander and Selivanov, Anton and Moloshnikov, Ivan and Rybka, Roman and Gryaznov, Artem and Sboeva, Sanna and Rylkov, Gleb},
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year={2021},
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publisher={Preprints}
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}
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'''
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