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