Add library_name: transformers to metadata

This PR adds the `library_name: transformers` tag to the model card metadata. The provided code examples clearly demonstrate the model's compatibility with the Hugging Face Transformers library. This addition enhances the model card's completeness and improves its discoverability on the Hugging Face Hub.

README.md

@@ -1,5 +1,6 @@
 ---
-license: cc-by-nc-sa-4.0
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
 datasets:
 - QCRI/LlamaLens-English
 - QCRI/LlamaLens-Arabic
@@ -8,8 +9,11 @@ language:
 - ar
 - en
 - hi
-base_model:
-- meta-llama/Llama-3.1-8B-Instruct
+license: cc-by-nc-sa-4.0
+metrics:
+- accuracy
+- f1
+- rouge
 pipeline_tag: text-generation
 tags:
 - Social-Media
@@ -17,12 +21,10 @@ tags:
 - Summarization
 - offensive-language
 - News-Genre
-metrics:
-- accuracy
-- f1
-- rouge
+library_name: transformers
 ---
-# LlamaLens: Specialized Multilingual LLM forAnalyzing News and Social Media Content
+
+# LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
 
 ## Overview
 LlamaLens is a specialized multilingual LLM designed for analyzing news and social media content. It focuses on 18 NLP tasks, leveraging 52 datasets across Arabic, English, and Hindi.
@@ -188,7 +190,6 @@ Below, we present  the performance of **L-Lens: LlamaLens** , where *"Eng"* refe
 |          Sarcasm Detection         | News-Headlines-Dataset-For-Sarcasm-Detection |     Acc    |   0.897  |         0.668         |         0.936         |         0.947        |           0.039          |
 |      Sentiment Classification      |               NewsMTSC-dataset               |    Ma-F1   |   0.817  |         0.628         |         0.751         |         0.748        |          -0.066          |
 |       Subjectivity Detection       |            clef2024-checkthat-lab            |    Ma-F1   |   0.744  |         0.535         |         0.642         |         0.628        |          -0.102          |
-|
 
 ---
 
@@ -203,12 +204,10 @@ Below, we present  the performance of **L-Lens: LlamaLens** , where *"Eng"* refe
 |         News Summarization         |                     xlsum                    |     R-2    |   0.136  |         0.078         |         0.171         |         0.170        |           0.035          |
 |    Offensive Language Detection    |          Offensive Speech Detection          |    Mi-F1   |   0.723  |         0.621         |         0.862         |         0.865        |           0.139          |
 |       Cyberbullying Detection      |                 MC_Hinglish1                 |     Acc    |   0.609  |         0.233         |         0.625         |         0.627        |           0.016          |
-|      Sentiment Classification      |              Sentiment Analysis              |     Acc    |   0.697  |         0.552         |         0.647         |         0.654        |          -0.050          
+|      Sentiment Classification      |              Sentiment Analysis              |     Acc    |   0.697  |         0.552         |         0.647         |         0.654        |          -0.050          |
 
 ## Paper  
-For an in-depth understanding, refer to our paper: [**LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content**](https://arxiv.org/pdf/2410.15308).
-
-
+For an in-depth understanding, refer to our paper: [LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content](https://arxiv.org/pdf/2410.15308).
 
 
 # License