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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 finetuned SciBERT model for context classification in scientific journals. |
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The model classifies intentions of the scientific text, based on the topic of their description. |
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It categorizes if the context explains the background, result or method of the paper. |
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The output classes based on the text are as follows: |
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</br> |
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Text describing related work, introduction and uses are classified as <b>background</b> |
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</br> |
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Methods and implementation details are classified as <b>method</b> |
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</br> |
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Results and analysis are classified as <b>result</b> |
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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 |