kevinscaria commited on
Commit
04c382d
1 Parent(s): e7df3ea

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +36 -0
README.md CHANGED
@@ -1,3 +1,39 @@
1
  ---
2
  license: mit
 
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  license: mit
3
+ tags:
4
+ - NLP
5
+ datasets:
6
+ - Yaxin/SemEval2014Task4Raw
7
+ metrics:
8
+ - f1
9
+ - precision
10
+ - recall
11
+ pipeline_tag: text2text-generation
12
  ---
13
+
14
+ # joint_tk-instruct-base-def-pos-neg-neut-restaurants
15
+ This model is finetuned for the Joint Task. The finetuning was carried out by adding prompts of the form:
16
+ - definition + 2 positive examples + 2 negative examples + 2 neutral examples
17
+
18
+ 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.**
19
+ 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
20
+ found [here](https://github.com/kevinscaria/InstructABSA).
21
+
22
+ For the Joint Task, this model is the current SOTA.
23
+
24
+ ## Training data
25
+
26
+ 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
27
+ from laptops and restaurant domains and their corresponding aspect term and polarity labels.
28
+
29
+ ### BibTeX entry and citation info
30
+
31
+ If you use this model in your work, please cite the following paper:
32
+
33
+ ```bibtex
34
+ @inproceedings{Scaria2023InstructABSAIL,
35
+ title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
36
+ author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
37
+ year={2023}
38
+ }
39
+ ```