---
widget:
- text: >-
    The third is the path length between long-range dependencies in the
    network. 
  example_title: Intent Classify
language:
- en
pipeline_tag: text-classification
---
This model is a fine-tuned version of SciBERT, specifically designed for context classification in scientific journals. 
Its primary function is to categorize the intentions of scientific texts based on the topic they describe.
The model assigns them to one of three classes: Background, Result, or Method. 
The Background class is used when the text provides relevant background information, such as theoretical concepts or previous 
research findings. The Result class is assigned to texts that describe the study's findings, including experimental data,
statistical analysis, or conclusions. 
Finally, the Method class is used for texts that explain the methodology or approach employed in the research.
The classes of the model output is defined below:
</br>
<ul>
<li>Text describing related work, introduction and uses are classified as <b>background</b></li>
<li>Methods and implementation details are classified as <b>method</b></li>
<li>Results and analysis are classified as <b>result</b></li>
</ul>
</br>
</br>
For finetuning, I have used dataset from Cohan et al. https://aclanthology.org/N19-1361.pdf