README.md

---
language: en
license: apache-2.0
base_model: roberta-base
pipeline_tag: text-classification
library_name: transformers
tags:
  - scientific-text
  - abstract-sections
widget:
  - text: "We conclude that early intervention significantly improves patient outcomes."
    example_title: "Conclusion sentence"
  - text: "Participants were randomly assigned to treatment and control groups."
    example_title: "Methods sentence"
  - text: "The aim of this study was to evaluate the efficacy of the new vaccine."
    example_title: "Objective sentence"
---

# RoBERTa Abstract-Section Classifier

[`roberta-base`](https://huggingface.co/roberta-base) fine-tuned to classify sentences from scientific abstracts into five rhetorical sections: `BACKGROUND`, `CONCLUSIONS`, `METHODS`, `OBJECTIVE`, `RESULTS`.

## Labels

| id | label | example |
|----|-------------|--------------------------------------------------|
| 0 | BACKGROUND | "Diabetes is a growing public health concern…" |
| 1 | CONCLUSIONS | "We conclude that early intervention improves outcomes." |
| 2 | METHODS | "Participants were randomly assigned to two groups…" |
| 3 | OBJECTIVE | "The aim of this study was to evaluate…" |
| 4 | RESULTS | "The treatment group showed a 40% reduction (p < 0.001)." |

## How to use

```python
from transformers import pipeline

clf = pipeline("text-classification", model="hongccccccc/roberta-abstract-section-classifier")
clf("The treatment group showed a 40% reduction in mortality compared with placebo.")
# [{'label': 'RESULTS', 'score': 0.99}]
```

## Training

- **Base model:** RoBERTa (Liu et al., 2019; `RobertaForSequenceClassification`, single-label, 5 classes)
- **Data:** 200,000 paper abstracts from PubMed (Canese and Weis, 2013), self-labeled with the five section categories
- **Fine-tuned:** January 2023, `transformers` 4.12.5 (original `training_args.bin` included)

## Evaluation

F1 score of **0.92** on a held-out 10% sample (details in Wright et al., 2022).