puzzz21/sci-intent-classify

1---2widget:3- text: >-4    The third is the path length between long-range dependencies in the5    network. 6  example_title: Intent Classify7language:8- en9pipeline_tag: text-classification10---11This model is a fine-tuned version of SciBERT, specifically designed for context classification in scientific journals. 12Its primary function is to categorize the intentions of scientific texts based on the topic they describe.13The model assigns them to one of three classes: Background, Result, or Method. 14The Background class is used when the text provides relevant background information, such as theoretical concepts or previous 15research findings. The Result class is assigned to texts that describe the study's findings, including experimental data,16statistical analysis, or conclusions. 17Finally, the Method class is used for texts that explain the methodology or approach employed in the research.18The classes of the model output is defined below:19</br>20<ul>21<li>Text describing related work, introduction and uses are classified as <b>background</b></li>22<li>Methods and implementation details are classified as <b>method</b></li>23<li>Results and analysis are classified as <b>result</b></li>24</ul>25</br>26</br>27For finetuning, I have used dataset from Cohan et al. https://aclanthology.org/N19-1361.pdf