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--- |
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language: |
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- en |
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datasets: |
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- pubmed |
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- ml4pubmed/pubmed-classification-20k |
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metrics: |
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- f1 |
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tags: |
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- text-classification |
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- document sections |
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- sentence classification |
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- document classification |
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- medical |
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- health |
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- biomedical |
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pipeline_tag: text-classification |
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widget: |
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- text: >- |
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many pathogenic processes and diseases are the result of an erroneous |
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activation of the complement cascade and a number of inhibitors of |
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complement have thus been examined for anti-inflammatory actions. |
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example_title: background example |
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- text: a total of 192 mi patients and 140 control persons were included. |
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example_title: methods example |
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- text: >- |
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mi patients had 18 % higher plasma levels of map44 (iqr 11-25 %) as compared |
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to the healthy control group (p < 0. 001.) |
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example_title: results example |
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- text: >- |
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the finding that a brief cb group intervention delivered by real-world |
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providers significantly reduced mdd onset relative to both brochure control |
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and bibliotherapy is very encouraging, although effects on continuous |
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outcome measures were small or nonsignificant and approximately half the |
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magnitude of those found in efficacy research, potentially because the |
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present sample reported lower initial depression. |
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example_title: conclusions example |
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- text: >- |
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in order to understand and update the prevalence of myopia in taiwan, a |
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nationwide survey was performed in 1995. |
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example_title: objective example |
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license: apache-2.0 |
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--- |
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# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section |
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- original model file name: textclassifer_BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pubmed_20k |
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- This is a fine-tuned checkpoint of `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext` for document section text classification |
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- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS, |
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## usage in python |
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install transformers as needed: |
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```bash |
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pip install -U transformers |
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``` |
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Run the following, changing the example text to your use case: |
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```python |
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from transformers import pipeline |
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model_tag = "ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section" |
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classifier = pipeline( |
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'text-classification', |
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model=model_tag, |
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) |
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prompt = """ |
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Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. |
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""" |
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classifier( |
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prompt, |
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) # classify the sentence |
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``` |
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## metadata |
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### training_metrics |
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- val_accuracy: 0.8678670525550842 |
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- val_matthewscorrcoef: 0.8222037553787231 |
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- val_f1score: 0.866841197013855 |
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- val_cross_entropy: 0.3674609065055847 |
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- epoch: 8.0 |
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- train_accuracy_step: 0.83984375 |
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- train_matthewscorrcoef_step: 0.7790813446044922 |
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- train_f1score_step: 0.837363600730896 |
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- train_cross_entropy_step: 0.39843088388442993 |
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- train_accuracy_epoch: 0.8538406491279602 |
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- train_matthewscorrcoef_epoch: 0.8031334280967712 |
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- train_f1score_epoch: 0.8521654605865479 |
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- train_cross_entropy_epoch: 0.4116102457046509 |
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- test_accuracy: 0.8578397035598755 |
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- test_matthewscorrcoef: 0.8091378808021545 |
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- test_f1score: 0.8566917181015015 |
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- test_cross_entropy: 0.3963385224342346 |
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- date_run: Apr-22-2022_t-19 |
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- huggingface_tag: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
