updated readme

README.md

@@ -27,7 +27,7 @@ As part of the supplemental materials, we have selected a few datasets (see belo
 ## AraDICE Collection
 AraDICE collection can accessed through [collection page](https://huggingface.co/collections/QCRI/aradice-6727765839bf89aa78e9f132)
 
-Individual dataset can also be accessed by the following link:
+Individual dataset can also be accessed by the following links:
 
 - [ArabicMMLU-lev](https://huggingface.co/datasets/QCRI/AraDICE-ArabicMMLU-lev)
 - [ArabicMMLU-egy](https://huggingface.co/datasets/QCRI/AraDICE-ArabicMMLU-egy)
@@ -38,6 +38,22 @@ Individual dataset can also be accessed by the following link:
 - [TruthfulQA](https://huggingface.co/datasets/QCRI/AraDiCE-TruthfulQA)
 - [AraDiCE-WinoGrande](https://huggingface.co/datasets/QCRI/AraDiCE-WinoGrande)
 
+## Machine Translation (MT) Models Used for AraDICE
+
+Along with AraDICE Collection, we provide Machine Translation (MT) models tailored for specific Arabic dialects. These models are designed to facilitate seamless translation from Modern Standard Arabic (MSA) into two prominent Arabic dialects: Levantine and Egyptian. The models leverage state-of-the-art neural translation methods to ensure high accuracy and contextual relevance.
+
+You can access and download the MT models using the following links:
+
+- **MSA to Levantine Dialect Model:** [AraDiCE-msa-to-lev](https://huggingface.co/QCRI/AraDiCE-msa-to-lev)
+This model translates text from MSA into the Levantine Arabic dialect, commonly spoken in countries like Lebanon, Syria, Jordan, and Palestine.
+
+- **MSA to Egyptian Dialect Model:** [AraDiCE-msa-to-egy](https://huggingface.co/QCRI/AraDiCE-msa-to-egy)
+This model enables translation from MSA into Egyptian Arabic, widely spoken and understood across Egypt and in other Arabic-speaking regions due to its cultural prominence.
+
+These models are hosted on Hugging Face for easy accessibility and integration into various applications.
+
+
+
 ## Dataset Statistics
 
 The datasets used in this study include: *i)* four existing Arabic datasets for understanding and generation: *Arabic Dialects Dataset (ADD)*, *ADI*, *QADI*, along with a dialectal response generation dataset, and *MADAR*; *ii)* seven datasets translated and post-edited into MSA and dialects (Levantine and Egyptian), which include *ArabicMMLU*, *BoolQ*, *PIQA*, *OBQA*, *Winogrande*, *Belebele*, and *TruthfulQA*; and *iii)* *AraDiCE-Culture*, an in-house developed regional Arabic cultural understanding dataset. Please find below the types of dataset and their statistics benchmarked in **AraDiCE**.
@@ -56,14 +72,8 @@ The AraDiCE dataset is intended to be used for benchmarking and evaluating large
 - Cultural context awareness in reasoning.
 
 ## Evaluation
-We have used [lm-harness](https://github.com/EleutherAI/lm-evaluation-harness) eval framework to for the benchmarking. We will soon release them. Stay tuned!!
-
-## Machine Translation Models
-We released the machine translation models that we trained to curate the synthetic benchmarks.
-
-- The MSA to Levantine model can be accessed [here](https://huggingface.co/QCRI/AraDiCE-msa-to-lev)
-- The MSA to Egyptian model can be accessed [here](https://huggingface.co/QCRI/AraDiCE-msa-to-egy)
-
+We have used [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) eval framework to for the benchmarking. It is under a [pull](https://github.com/EleutherAI/lm-evaluation-harness/pull/2507) request on *lm-evaluation-harness* at this moment.
+<!-- We will soon release them. Stay tuned!! -->
 
 
 ## License
@@ -72,7 +82,7 @@ The dataset is distributed under the **Creative Commons Attribution-NonCommercia
 
 
 ## Citation
-Please find the paper <a href="https://arxiv.org/pdf/2409.11404" target="_blank" style="margin-right: 15px; margin-left: 10px">here.</a>If you use all or any specific dataset in this collection, please make sure if also cite original dataset paper. You will find the citations in our paper.
+Please find the paper <a href="https://arxiv.org/pdf/2409.11404" target="_blank" style="margin-right: 15px; margin-left: 10px">here</a>, which is accepted at [COLING 2025](https://coling2025.org/). If you use all or any specific dataset in this collection, please make sure if also cite original dataset paper. You will find the citations in our paper.
 
 ```
 @article{mousi2024aradicebenchmarksdialectalcultural,