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