Improve model card: Add paper abstract for context

This PR improves the model card by adding the paper's abstract to the model description section. This provides more comprehensive context regarding the Voxlect benchmark and the model's role within it. The existing arXiv paper link and GitHub repository link are retained as they are already accurate. No project page link is added as none was provided in the context.

README.md

@@ -3,6 +3,7 @@ base_model:
 - facebook/mms-lid-256
 language:
 - fr
+library_name: transformers
 license: openrail
 metrics:
 - accuracy
@@ -11,17 +12,18 @@ tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
 - speaker_dialect_classification
-library_name: transformers
 ---
 
 # MMS-LID-256 for French Dialect Classification
 
 # Model Description
-This model includes the implementation of French dialect classification described in <a href="https://arxiv.org/abs/2508.01691"><strong>**Voxlect: A Speech Foundation Model Benchmark for Modeling Dialect and Regional Languages Around the Globe**</strong></a>
+This model includes the implementation of French dialect classification described in [**Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe**](https://arxiv.org/abs/2508.01691).
+
+**Abstract:** We present Voxlect, a novel benchmark for modeling dialects and regional languages worldwide using speech foundation models. Specifically, we report comprehensive benchmark evaluations on dialects and regional language varieties in English, Arabic, Mandarin and Cantonese, Tibetan, Indic languages, Thai, Spanish, French, German, Brazilian Portuguese, and Italian. Our study used over 2 million training utterances from 30 publicly available speech corpora that are provided with dialectal information. We evaluate the performance of several widely used speech foundation models in classifying speech dialects. We assess the robustness of the dialectal models under noisy conditions and present an error analysis that highlights modeling results aligned with geographic continuity. In addition to benchmarking dialect classification, we demonstrate several downstream applications enabled by Voxlect. Specifically, we show that Voxlect can be applied to augment existing speech recognition datasets with dialect information, enabling a more detailed analysis of ASR performance across dialectal variations. Voxlect is also used as a tool to evaluate the performance of speech generation systems. Voxlect is publicly available with the license of the RAIL family at: this https URL .
 
 Github repository: https://github.com/tiantiaf0627/voxlect
 
-The included French dialects are with speakers from: 
+The included French dialects are with speakers from:
 ```
 [
   "Africa",
@@ -35,7 +37,7 @@ The included French dialects are with speakers from:
 
 ## Download repo
 ```bash
-git clone git@github.com:tiantiaf0627/voxvoxlect
+git clone git@github.com:tiantiaf0627/voxlect
 ```
 ## Install the package
 ```bash