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README.md
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## Results
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Below, we present
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## File Format
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## Results
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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).
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| **Task** | **Dataset** | **Metric** | **SOTA** | **Base** | **L-Lens-Eng** | **L-Lens-Native** | **Δ (L-Lens (Eng) - SOTA)** |
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|:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
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| Attentionworthiness Detection | CT22Attentionworthy | W-F1 | 0.412 | 0.158 | 0.425 | 0.454 | 0.013 |
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| Checkworthiness Detection | CT24_checkworthy | F1_Pos | 0.569 | 0.610 | 0.502 | 0.509 | -0.067 |
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| Claim Detection | CT22Claim | Acc | 0.703 | 0.581 | 0.734 | 0.756 | 0.031 |
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| Cyberbullying Detection | ArCyc_CB | Acc | 0.863 | 0.766 | 0.870 | 0.833 | 0.007 |
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| Emotion Detection | Emotional-Tone | W-F1 | 0.658 | 0.358 | 0.705 | 0.736 | 0.047 |
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| Emotion Detection | NewsHeadline | Acc | 1.000 | 0.406 | 0.480 | 0.458 | -0.520 |
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| Factuality | Arafacts | Mi-F1 | 0.850 | 0.210 | 0.771 | 0.738 | -0.079 |
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| Factuality | COVID19Factuality | W-F1 | 0.831 | 0.492 | 0.800 | 0.840 | -0.031 |
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| Harmfulness Detection | CT22Harmful | F1_Pos | 0.557 | 0.507 | 0.523 | 0.535 | -0.034 |
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| Hate Speech Detection | annotated-hatetweets-4-classes | W-F1 | 0.630 | 0.257 | 0.526 | 0.517 | -0.104 |
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| Hate Speech Detection | OSACT4SubtaskB | Mi-F1 | 0.950 | 0.819 | 0.955 | 0.955 | 0.005 |
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| News Categorization | ASND | Ma-F1 | 0.770 | 0.587 | 0.919 | 0.929 | 0.149 |
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| News Categorization | SANADAkhbarona-news-categorization | Acc | 0.940 | 0.784 | 0.954 | 0.953 | 0.014 |
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| News Categorization | SANADAlArabiya-news-categorization | Acc | 0.974 | 0.893 | 0.987 | 0.985 | 0.013 |
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| News Categorization | SANADAlkhaleej-news-categorization | Acc | 0.986 | 0.865 | 0.984 | 0.982 | -0.002 |
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| News Categorization | UltimateDataset | Ma-F1 | 0.970 | 0.376 | 0.865 | 0.880 | -0.105 |
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| News Credibility | NewsCredibilityDataset | Acc | 0.899 | 0.455 | 0.935 | 0.933 | 0.036 |
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| News Summarization | xlsum | R-2 | 0.137 | 0.034 | 0.129 | 0.130 | -0.009 |
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| Offensive Language Detection | ArCyc_OFF | Ma-F1 | 0.878 | 0.489 | 0.877 | 0.879 | -0.001 |
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| Offensive Language Detection | OSACT4SubtaskA | Ma-F1 | 0.905 | 0.782 | 0.896 | 0.882 | -0.009 |
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| Propaganda Detection | ArPro | Mi-F1 | 0.767 | 0.597 | 0.747 | 0.731 | -0.020 |
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| Sarcasm Detection | ArSarcasm-v2 | F1_Pos | 0.584 | 0.477 | 0.520 | 0.542 | -0.064 |
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| Sentiment Classification | ar_reviews_100k | F1_Pos | -- | 0.681 | 0.785 | 0.779 | -- |
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| Sentiment Classification | ArSAS | Acc | 0.920 | 0.603 | 0.800 | 0.804 | -0.120 |
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| Stance Detection | stance | Ma-F1 | 0.767 | 0.608 | 0.926 | 0.881 | 0.159 |
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| Stance Detection | Mawqif-Arabic-Stance-main | Ma-F1 | 0.789 | 0.764 | 0.853 | 0.826 | 0.065 |
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| Subjectivity Detection | ThatiAR | f1_pos | 0.800 | 0.562 | 0.441 | 0.383 | -0.359 |
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## File Format
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