Update README.md

README.md

@@ -1,68 +1,64 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- token-classification
+- text-classification
+language:
+- ar
+- da
+- de
+- en
+- es
+- fr
+- hi
+- hr
+- id
+- ja
+- ko
+- nl
+- pt
+- ru
+- sk
+- sv
+- sw
+- th
+- tr
+- vi
+- zh
+tags:
+- aspect-based-sentiment-analysis
+size_categories:
+- 100K<n<1M
+---
 # M-ABSA
 
-This repo contains the data and code for our paper ****M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis****.
+This repo contains the data for our paper ****M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis****.
 
 [![arXiv](https://img.shields.io/badge/arXiv-2502.11824-b31b1b.svg)](https://arxiv.org/abs/2502.11824)
 
 
 # Data Description:
+This is a dataset suitable for the __multilingual ABSA__ task with __triplet extraction__.
 
 All datasets are stored in the data/ folder:
 
-- All dataset contains 7 domains. 
+- All dataset contains __7__ domains. 
 
  ```
 domains = ["coursera", "hotel", "laptop", "restaurant", "phone", "sight", "food"]
 ``` 
-- Each dataset contains 21 languages.
+- Each dataset contains __21__ languages.
 ```
 langs = ["ar", "da", "de", "en", "es", "fr", "hi", "hr", "id", "ja", "ko", "nl", "pt", "ru", "sk", "sv", "sw", "th", "tr", "vi", "zh"]
 ```
-- Each dataset is divided into training, validation, and test sets. Each sentence is separated by __"####"__, with the first part being the sentence and the second part being the corresponding triplet. Here is an example, where the triplet includes __[aspect, category, sentiment]__.
 
-```
-This coffee brews up a nice medium roast with exotic floral and berry notes .####[['coffee', 'food quality', 'positive', 'nice']]
-```
-
-## Requirements
-
-We recommend you to install the specified version of the following packages:
-
-- transformers==4.0.0
--  sentencepiece==0.1.91
--  pytorch_lightning==0.8.1
-
-## Quick Start for the Baseline
-
-- Set up the environment as described in the above section.
-- Download the pre-trained mT5-base model from [https://huggingface.co/google/mt5-base](https://huggingface.co/google/mt5-base) and place it under the folder mT5-base/ .
-- Run command bash run.sh, which train the model on source language under UABSA/TASD task.
-- Run command bash evaluate.sh, which test the model on target language under UABSA/TASD task.
-
-****Detailed Usage****
-
-We conduct experiments on two ABSA subtasks with M-ABSA dataset in the paper, you can change the parameters in run.sh to try them:
+- The labels contain triplets with __[aspect term, aspect category, sentiment polarity]__. Each sentence is separated by __"####"__, with the first part being the sentence and the second part being the corresponding triplet. Here is an example, where the triplet includes __[aspect term, aspect category, sentiment polarity]__.
 
 ```
-python main.py --task tasd \
-            --dataset hotel \
-            --model_name_or_path mt5-base \
-            --paradigm extraction \
-            --n_gpu 0 \
-            --do_train \
-            --do_direct_eval \
-            --train_batch_size 16 \
-            --gradient_accumulation_steps 2 \
-            --eval_batch_size 16 \
-            --learning_rate 3e-4 \
-            --num_train_epochs 5
+This coffee brews up a nice medium roast with exotic floral and berry notes .####[['coffee', 'food quality', 'positive']]
 ```
 
-- $task refers to one of the ABSA task in [uabsa, tasd]
-- $dataset refers to one of the seven datasets in [food, restaurant, coursera, laptop, sight, phone, hotel]
+- Each dataset is divided into training, validation, and test sets. 
 
 
 ## Citation
@@ -78,5 +74,4 @@ If the code or dataset is used in your research, please star our repo and cite o
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2502.11824}, 
 }
-```
-
+```