Update README.md

README.md

@@ -149,7 +149,7 @@ configs:
 
 ## Overview
 
-LlamaLens is a specialized multilingual LLM designed for analyzing news and social media content. It focuses on 19 NLP tasks, leveraging 52 datasets across Arabic, English, and Hindi.
+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.
 
 <p align="center"> <img src="https://huggingface.co/datasets/QCRI/LlamaLens-Arabic/resolve/main/capablities_tasks_datasets.png" style="width: 40%;" id="title-icon"> </p>
 
@@ -160,7 +160,7 @@ This repo includes scripts needed to run our full pipeline, including data prepr
 ### Features
 
 - Multilingual support (Arabic, English, Hindi)
-- 19 NLP tasks with 52 datasets
+- 18 NLP tasks with 52 datasets
 - Optimized for news and social media content analysis
 
 ## 📂 Dataset Overview
@@ -182,21 +182,20 @@ This repo includes scripts needed to run our full pipeline, including data prepr
 
 ## Results
 
-Below, we present the performance of **LlamaLens** in **Hindi** compared to existing SOTA (if available) and the Llama-Instruct baseline, The “Δ” (Delta) column here is 
-calculated as **(LLamalens – SOTA)**.
+Below, we present  the performance of **L-Lens: LlamaLens** , where *"Eng"* refers to the English-instructed model and *"Native"* refers to the model trained with native language instructions. The results are compared against the SOTA (where available) and the Base: **Llama-Instruct 3.1 baseline**. The **Δ** (Delta) column indicates the difference between LlamaLens and the SOTA performance, calculated as (LlamaLens – SOTA).
 
----
 
-| **Task**               | **Dataset**            | **Metric** | **SOTA** | **Llama-instruct** | **LLamalens** | **Δ** (LLamalens - SOTA) |
-|------------------------|------------------------|-----------:|--------:|--------------------:|--------------:|------------------------------:|
-| NLI                    | NLI_dataset           | W-F1       | 0.646   | 0.633              | 0.655         | 0.009                        |
-| News Summarization     | xlsum                 | R-2        | 0.136   | 0.078              | 0.117         | -0.019                       |
-| Sentiment              | Sentiment Analysis    | Acc        | 0.697   | 0.552              | 0.669         | -0.028                       |
-| Factuality             | fake-news             | Mi-F1      | –       | 0.759              | 0.713         | –                            |
-| Hate Speech            | hate-speech-detection | Mi-F1      | 0.639   | 0.750              | 0.994         | 0.355                        |
-| Hate Speech            | Hindi-Hostility       | W-F1       | 0.841   | 0.469              | 0.720         | -0.121                       |
-| Offensive              | Offensive Speech      | Mi-F1      | 0.723   | 0.621              | 0.847         | 0.124                        |
-| Cyberbullying          | MC_Hinglish1          | Acc        | 0.609   | 0.233              | 0.587         | -0.022                       |
+---
+|              **Task**              |                  **Dataset**                 | **Metric** | **SOTA** | **Base** | **L-Lens-Eng** | **L-Lens-Native** | **Δ (L-Lens (Eng) - SOTA)** |
+|:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
+|             Factuality             |                   fake-news                  |    Mi-F1   |    --    |         0.759         |         0.994         |         0.993        |            --            |
+|        Hate Speech Detection       |             hate-speech-detection            |    Mi-F1   |   0.639  |         0.750         |         0.963         |         0.963        |           0.324          |
+|        Hate Speech Detection       |   Hindi-Hostility-Detection-CONSTRAINT-2021  |    W-F1    |   0.841  |         0.469         |         0.753         |         0.753        |          -0.088          |
+|     Natural Language Inference     |          Natural Language Inference          |    W-F1    |   0.646  |         0.633         |         0.568         |         0.679        |          -0.078          |
+|         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          
 
 
 ## File Format