Update README.md

README.md

@@ -1,3 +1,39 @@
 ---
 license: mit
+tags:
+- NLP
+datasets:
+- Yaxin/SemEval2014Task4Raw
+metrics:
+- f1
+- precision
+- recall
+pipeline_tag: text2text-generation
 ---
+
+# ate_tk-instruct-base-def-pos-neg-neut-combined
+This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form: 
+ - definition + 2 positive examples + 2 negative examples + 2 neutral examples.
+
+The prompt is prepended onto each input review. It is important to note that **this model output was finetuned on samples from both laptops and restaurants domains.**
+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 
+found [here](https://github.com/kevinscaria/InstructABSA).
+
+For the ATE subtask, this model is the current SOTA.
+
+## Training data
+
+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 
+from laptops and restaurant domains and their corresponding aspect term and polarity labels.
+
+### BibTeX entry and citation info
+
+If you use this model in your work, please cite the following paper:
+
+```bibtex
+@inproceedings{Scaria2023InstructABSAIL,
+  title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
+  author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
+  year={2023}
+}
+```