README.md

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widget:
- text: >-
    As side benefit, self-attention could yield more interpretable models. 
  example_title: Sentiment Classify
language:
- en
pipeline_tag: text-classification
---
This model has been fine-tuned on Scibert specifically for sentiment classification in scientific texts. Its primary task is to categorize the sentiment expressed by the author based on the context of the sentence. The model classifies the sentiment into one of three classes: positive, negative, or neutral. The positive class is assigned when the author expresses a positive sentiment in the text, while the negative class is used when a negative sentiment is conveyed. The neutral class is assigned when the text does not exhibit any strong positive or negative sentiment.
This model outputs following classnames according to the sentiment:
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<ul>
  <li>
    Positive sentiment in context is classified as <b>p</b>
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    Negative sentiment in context is classified as <b>n</b>
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    Neutral sentiment in context is classified as (other) <b>o</b>
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</ul>
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The model achieved F1 score of 0.72 and an accuracy score of 0.73, with the manually annoted dataset: https://huggingface.co/datasets/puzzz21/sci-sentiment-annotated-dataset . 
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For finetuning, the publicly available dataset on context identification from Angrosh et al. https://dl.acm.org/doi/10.1145/1816123.1816168 is used.