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  license: mit
 
 
 
 
 
 
 
 
 
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  license: mit
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+ tags:
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+ - NLP
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+ datasets:
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+ - Yaxin/SemEval2014Task4Raw
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+ metrics:
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+ - f1
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+ - precision
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+ - recall
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+ pipeline_tag: text2text-generation
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  ---
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+
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+ # atsc_tk-instruct-base-def-pos-neg-neut-restaurants
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+ This model is finetuned for the Aspect Term Sentiment Classification (ATSC) subtask. The finetuning was carried out by adding prompts of the form:
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+ - definition + 2 positive examples + 2 negative examples = 2 neutral examples
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+
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+ The prompt is prepended onto each input review. It is important to note that **this model output was finetuned on samples from the restaurants domains.**
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+ The code for the official implementation of the paper [**InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis**](https://arxiv.org/abs/2302.08624) can be
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+ found [here](https://github.com/kevinscaria/InstructABSA).
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+
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+ For the ATSC subtask, this model has a competitive performance with the current SOTA.
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+
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+ ## Training data
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+
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+ InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This [dataset](https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools) consists of reviews
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+ from laptops and restaurant domains and their corresponding aspect term and polarity labels.
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+
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+ ### BibTeX entry and citation info
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+
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+ If you use this model in your work, please cite the following paper:
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+
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+ ```bibtex
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+ @inproceedings{Scaria2023InstructABSAIL,
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+ title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
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+ author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
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+ year={2023}
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+ }
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+ ```