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Datasets:
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LlamaLens-Arabic

Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
Arabic
Size:
1M - 10M
ArXiv:
Tags:
Social Media
News Media
Sentiment
Stance
Emotion
Libraries:
Datasets
pandas
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metadata
license: cc-by-nc-sa-4.0
task_categories:
  - text-classification
language:
  - ar
tags:
  - Social Media
  - News Media
  - Sentiment
  - Stance
  - Emotion
pretty_name: >-
  LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media
  Content -- Arabic
size_categories:
  - 10K<n<100K
dataset_info:
  - config_name: SANADAkhbarona-news-categorization
    splits:
      - name: train
        num_examples: 62210
      - name: dev
        num_examples: 7824
      - name: test
        num_examples: 7824
  - config_name: CT22Harmful
    splits:
      - name: train
        num_examples: 2484
      - name: dev
        num_examples: 1076
      - name: test
        num_examples: 1201
  - config_name: Mawqif-Arabic-Stance-main
    splits:
      - name: train
        num_examples: 3162
      - name: dev
        num_examples: 950
      - name: test
        num_examples: 560
  - config_name: CT22Claim
    splits:
      - name: train
        num_examples: 3513
      - name: dev
        num_examples: 339
      - name: test
        num_examples: 1248
  - config_name: annotated-hatetweets-4-classes
    splits:
      - name: train
        num_examples: 210525
      - name: dev
        num_examples: 90543
      - name: test
        num_examples: 100564
  - config_name: ar_reviews_100k
    splits:
      - name: train
        num_examples: 69998
      - name: dev
        num_examples: 10000
      - name: test
        num_examples: 20000
  - config_name: Arafacts
    splits:
      - name: train
        num_examples: 4354
      - name: dev
        num_examples: 623
      - name: test
        num_examples: 1245
  - config_name: OSACT4SubtaskA
    splits:
      - name: train
        num_examples: 4780
      - name: dev
        num_examples: 2047
      - name: test
        num_examples: 1827
  - config_name: SANADAlArabiya-news-categorization
    splits:
      - name: train
        num_examples: 56967
      - name: dev
        num_examples: 7120
      - name: test
        num_examples: 7123
  - config_name: ArPro
    splits:
      - name: train
        num_examples: 6002
      - name: dev
        num_examples: 672
      - name: test
        num_examples: 1326
  - config_name: xlsum
    splits:
      - name: train
        num_examples: 37425
      - name: dev
        num_examples: 4689
      - name: test
        num_examples: 4689
  - config_name: ArSarcasm-v2
    splits:
      - name: train
        num_examples: 8749
      - name: dev
        num_examples: 3761
      - name: test
        num_examples: 2996
  - config_name: COVID19Factuality
    splits:
      - name: train
        num_examples: 3513
      - name: dev
        num_examples: 339
      - name: test
        num_examples: 988
  - config_name: Emotional-Tone
    splits:
      - name: train
        num_examples: 7024
      - name: dev
        num_examples: 1005
      - name: test
        num_examples: 2009
  - config_name: ans-claim
    splits:
      - name: train
        num_examples: 3185
      - name: dev
        num_examples: 906
      - name: test
        num_examples: 456
  - config_name: ArCyc_OFF
    splits:
      - name: train
        num_examples: 3138
      - name: dev
        num_examples: 450
      - name: test
        num_examples: 900
  - config_name: CT24_checkworthy
    splits:
      - name: train
        num_examples: 7333
      - name: dev
        num_examples: 1093
      - name: test
        num_examples: 610
  - config_name: stance
    splits:
      - name: train
        num_examples: 2652
      - name: dev
        num_examples: 755
      - name: test
        num_examples: 379
  - config_name: NewsHeadline
    splits:
      - name: train
        num_examples: 939
      - name: dev
        num_examples: 160
      - name: test
        num_examples: 323
  - config_name: NewsCredibilityDataset
    splits:
      - name: train
        num_examples: 8671
      - name: dev
        num_examples: 1426
      - name: test
        num_examples: 2730
  - config_name: UltimateDataset
    splits:
      - name: train
        num_examples: 133036
      - name: dev
        num_examples: 19269
      - name: test
        num_examples: 38456
  - config_name: ThatiAR
    splits:
      - name: train
        num_examples: 2446
      - name: dev
        num_examples: 467
      - name: test
        num_examples: 748
  - config_name: ArSAS
    splits:
      - name: train
        num_examples: 13883
      - name: dev
        num_examples: 1987
      - name: test
        num_examples: 3976
  - config_name: CT22Attentionworthy
    splits:
      - name: train
        num_examples: 2479
      - name: dev
        num_examples: 1071
      - name: test
        num_examples: 1186
  - config_name: ASND
    splits:
      - name: train
        num_examples: 74496
      - name: dev
        num_examples: 11136
      - name: test
        num_examples: 21942
  - config_name: OSACT4SubtaskB
    splits:
      - name: train
        num_examples: 4778
      - name: dev
        num_examples: 2048
      - name: test
        num_examples: 1827
  - config_name: ArCyc_CB
    splits:
      - name: train
        num_examples: 3145
      - name: dev
        num_examples: 451
      - name: test
        num_examples: 900
  - config_name: SANADAlkhaleej-news-categorization
    splits:
      - name: train
        num_examples: 36391
      - name: dev
        num_examples: 4550
      - name: test
        num_examples: 4550
configs:
  - config_name: SANADAkhbarona-news-categorization
    data_files:
      - split: test
        path: SANADAkhbarona-news-categorization/test.json
      - split: dev
        path: SANADAkhbarona-news-categorization/dev.json
      - split: train
        path: SANADAkhbarona-news-categorization/train.json
  - config_name: CT22Harmful
    data_files:
      - split: test
        path: CT22Harmful/test.json
      - split: dev
        path: CT22Harmful/dev.json
      - split: train
        path: CT22Harmful/train.json
  - config_name: Mawqif-Arabic-Stance-main
    data_files:
      - split: test
        path: Mawqif-Arabic-Stance-main/test.json
      - split: dev
        path: Mawqif-Arabic-Stance-main/dev.json
      - split: train
        path: Mawqif-Arabic-Stance-main/train.json
  - config_name: CT22Claim
    data_files:
      - split: test
        path: CT22Claim/test.json
      - split: dev
        path: CT22Claim/dev.json
      - split: train
        path: CT22Claim/train.json
  - config_name: annotated-hatetweets-4-classes
    data_files:
      - split: test
        path: annotated-hatetweets-4-classes/test.json
      - split: dev
        path: annotated-hatetweets-4-classes/dev.json
      - split: train
        path: annotated-hatetweets-4-classes/train.json
  - config_name: ar_reviews_100k
    data_files:
      - split: test
        path: ar_reviews_100k/test.json
      - split: dev
        path: ar_reviews_100k/dev.json
      - split: train
        path: ar_reviews_100k/train.json
  - config_name: Arafacts
    data_files:
      - split: test
        path: Arafacts/test.json
      - split: dev
        path: Arafacts/dev.json
      - split: train
        path: Arafacts/train.json
  - config_name: OSACT4SubtaskA
    data_files:
      - split: test
        path: OSACT4SubtaskA/test.json
      - split: dev
        path: OSACT4SubtaskA/dev.json
      - split: train
        path: OSACT4SubtaskA/train.json
  - config_name: SANADAlArabiya-news-categorization
    data_files:
      - split: test
        path: SANADAlArabiya-news-categorization/test.json
      - split: dev
        path: SANADAlArabiya-news-categorization/dev.json
      - split: train
        path: SANADAlArabiya-news-categorization/train.json
  - config_name: ArPro
    data_files:
      - split: test
        path: ArPro/test.json
      - split: dev
        path: ArPro/dev.json
      - split: train
        path: ArPro/train.json
  - config_name: xlsum
    data_files:
      - split: test
        path: xlsum/test.json
      - split: dev
        path: xlsum/dev.json
      - split: train
        path: xlsum/train.json
  - config_name: ArSarcasm-v2
    data_files:
      - split: test
        path: ArSarcasm-v2/test.json
      - split: dev
        path: ArSarcasm-v2/dev.json
      - split: train
        path: ArSarcasm-v2/train.json
  - config_name: COVID19Factuality
    data_files:
      - split: test
        path: COVID19Factuality/test.json
      - split: dev
        path: COVID19Factuality/dev.json
      - split: train
        path: COVID19Factuality/train.json
  - config_name: Emotional-Tone
    data_files:
      - split: test
        path: Emotional-Tone/test.json
      - split: dev
        path: Emotional-Tone/dev.json
      - split: train
        path: Emotional-Tone/train.json
  - config_name: ans-claim
    data_files:
      - split: test
        path: ans-claim/test.json
      - split: dev
        path: ans-claim/dev.json
      - split: train
        path: ans-claim/train.json
  - config_name: ArCyc_OFF
    data_files:
      - split: test
        path: ArCyc_OFF/test.json
      - split: dev
        path: ArCyc_OFF/dev.json
      - split: train
        path: ArCyc_OFF/train.json
  - config_name: CT24_checkworthy
    data_files:
      - split: test
        path: CT24_checkworthy/test.json
      - split: dev
        path: CT24_checkworthy/dev.json
      - split: train
        path: CT24_checkworthy/train.json
  - config_name: stance
    data_files:
      - split: test
        path: stance/test.json
      - split: dev
        path: stance/dev.json
      - split: train
        path: stance/train.json
  - config_name: NewsHeadline
    data_files:
      - split: test
        path: NewsHeadline/test.json
      - split: dev
        path: NewsHeadline/dev.json
      - split: train
        path: NewsHeadline/train.json
  - config_name: NewsCredibilityDataset
    data_files:
      - split: test
        path: NewsCredibilityDataset/test.json
      - split: dev
        path: NewsCredibilityDataset/dev.json
      - split: train
        path: NewsCredibilityDataset/train.json
  - config_name: UltimateDataset
    data_files:
      - split: test
        path: UltimateDataset/test.json
      - split: dev
        path: UltimateDataset/dev.json
      - split: train
        path: UltimateDataset/train.json
  - config_name: ThatiAR
    data_files:
      - split: test
        path: ThatiAR/test.json
      - split: dev
        path: ThatiAR/dev.json
      - split: train
        path: ThatiAR/train.json
  - config_name: ArSAS
    data_files:
      - split: test
        path: ArSAS/test.json
      - split: dev
        path: ArSAS/dev.json
      - split: train
        path: ArSAS/train.json
  - config_name: CT22Attentionworthy
    data_files:
      - split: test
        path: CT22Attentionworthy/test.json
      - split: dev
        path: CT22Attentionworthy/dev.json
      - split: train
        path: CT22Attentionworthy/train.json
  - config_name: ASND
    data_files:
      - split: test
        path: ASND/test.json
      - split: dev
        path: ASND/dev.json
      - split: train
        path: ASND/train.json
  - config_name: OSACT4SubtaskB
    data_files:
      - split: test
        path: OSACT4SubtaskB/test.json
      - split: dev
        path: OSACT4SubtaskB/dev.json
      - split: train
        path: OSACT4SubtaskB/train.json
  - config_name: ArCyc_CB
    data_files:
      - split: test
        path: ArCyc_CB/test.json
      - split: dev
        path: ArCyc_CB/dev.json
      - split: train
        path: ArCyc_CB/train.json
  - config_name: SANADAlkhaleej-news-categorization
    data_files:
      - split: test
        path: SANADAlkhaleej-news-categorization/test.json
      - split: dev
        path: SANADAlkhaleej-news-categorization/dev.json
      - split: train
        path: SANADAlkhaleej-news-categorization/train.json

LlamaLens: Specialized Multilingual LLM Dataset

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.

LlamaLens

This repo includes scripts needed to run our full pipeline, including data preprocessing and sampling, instruction dataset creation, model fine-tuning, inference and evaluation.

Features

  • Multilingual support (Arabic, English, Hindi)
  • 18 NLP tasks with 52 datasets
  • Optimized for news and social media content analysis

๐Ÿ“‚ Dataset Overview

Arabic Datasets

Task Dataset # Labels # Train # Test # Dev
Attentionworthiness CT22Attentionworthy 9 2,470 1,186 1,071
Checkworthiness CT24_T1 2 22,403 500 1,093
Claim CT22Claim 2 3,513 1,248 339
Cyberbullying ArCyc_CB 2 3,145 900 451
Emotion Emotional-Tone 8 7,024 2,009 1,005
Emotion NewsHeadline 7 939 323 160
Factuality Arafacts 5 4,354 1,245 623
Factuality COVID19Factuality 2 3,513 988 339
Harmful CT22Harmful 2 2,484 1,201 1,076
Hate Speech annotated-hatetweets-4-classes 4 210,526 100,565 90,544
Hate Speech OSACT4SubtaskB 2 4,778 1,827 2,048
News Genre Categorization ASND 10 74,496 21,942 11,136
News Genre Categorization SANADAkhbarona 7 62,210 7,824 7,824
News Genre Categorization SANADAlArabiya 6 56,967 7,123 7,120
News Genre Categorization SANADAlkhaleej 7 36,391 4,550 4,550
News Genre Categorization UltimateDataset 10 133,036 38,456 19,269
News Credibility NewsCredibilityDataset 2 8,671 2,730 1,426
Summarization xlsum -- 37,425 4,689 4,689
Offensive Language ArCyc_OFF 2 3,138 900 450
Offensive Language OSACT4SubtaskA 2 4,780 1,827 2,047
Propaganda ArPro 2 6,002 1,326 672
Sarcasm ArSarcasm-v2 2 8,749 2,996 3,761
Sentiment ar_reviews_100k 3 69,998 20,000 10,000
Sentiment ArSAS 4 13,883 3,976 1,987
Stance Mawqif-Arabic-Stance-main 2 3,162 560 950
Stance stance 3 2,652 379 755
Subjectivity ThatiAR 2 2,446 748 467

Results

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 ฮ” (Delta) column indicates the difference between LlamaLens and the SOTA performance, calculated as (LlamaLens โ€“ SOTA).

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

File Format

Each JSONL file in the dataset follows a structured format with the following fields:

  • id: Unique identifier for each data entry.
  • original_id: Identifier from the original dataset, if available.
  • input: The original text that needs to be analyzed.
  • output: The label assigned to the text after analysis.
  • dataset: Name of the dataset the entry belongs.
  • task: The specific task type.
  • lang: The language of the input text.
  • instructions: A brief set of instructions describing how the text should be labeled.

Example entry in JSONL file:

{
    "id": "c64503bb-9253-4f58-aef8-9b244c088b15",
    "original_id": "1,722,643,241,323,950,300",
    "input": "ูŠุฑูŠุฏูˆู† ุชูˆุฑูŠุท ุงู„ุณู„ุทุฉ ุงู„ูู„ุณุทูŠู†ูŠุฉ ููŠ ุงู„ุถูุฉ ูˆุฏู‚ ุขุฎุฑ ู…ุณู…ุงุฑ ููŠ ู†ุนุด ู…ุง ุชุจู‚ู‰ ู…ู† ู‡ูˆูŠุชู†ุง ุงู„ูู„ุณุทูŠู†ูŠุฉุŒ ูƒู…ุง ุชู… ุชูˆุฑูŠุท ุบุฒุฉ. ูŠุฑูŠุฏูˆู† ุฅุนู„ุงู† ูƒูุงุญ ู…ุณู„ุญ ู…ู† ุทุฑู ุงู„ุฃุฌู‡ุฒุฉ ุงู„ุฃู…ู†ูŠุฉ ุงู„ูู„ุณุทูŠู†ูŠุฉ ุนู„ู†ุงู‹! ู„ูƒู† ู…ุง ูŠุนู„ู…ูˆู†ู‡ ูˆู…ุง ูŠุฑูˆู†ู‡ ูˆู„ุง ูŠุฑูŠุฏูˆู† ุงู„ุชุญุฏุซ ุจู‡ุŒ ุฃู† ุฃุจู†ุงุก ุงู„ุฃุฌู‡ุฒุฉ ุงู„ุฃู…ู†ูŠุฉ ููŠ ุงู„ู†ู‡ุงุฑ ูŠูƒูˆู†ูˆู† ุนุณูƒุฑูŠูŠู†... https://t.co/qF2Fjh24hV https://t.co/1UicLkDd52",
    "output": "checkworthy",
    "dataset": "Checkworthiness",
    "task": "Checkworthiness",
    "lang": "ar",
    "instructions": "Identify if the given factual claim is 'checkworthy' or 'not-checkworthy'. Return only the label without any explanation, justification, or additional text."
}

Model

LlamaLens on Hugging Face

Replication Scripts

LlamaLens GitHub Repository

๐Ÿ“ข Citation

If you use this dataset, please cite our paper:

@article{kmainasi2024llamalensspecializedmultilingualllm,
  title={LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content},
  author={Mohamed Bayan Kmainasi and Ali Ezzat Shahroor and Maram Hasanain and Sahinur Rahman Laskar and Naeemul Hassan and Firoj Alam},
  year={2024},
  journal={arXiv preprint arXiv:2410.15308},
  volume={},
  number={},
  pages={},
  url={https://arxiv.org/abs/2410.15308},
  eprint={2410.15308},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}