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Datasets:
QCRI
/
LlamaLens-English

Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
1M - 10M
ArXiv:
Tags:
Social Media
News Media
Sentiment
Stance
Emotion
Libraries:
Datasets
pandas
License:
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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 -- English
size_categories:
  - 10K<n<100K
dataset_info:
  - config_name: QProp
    splits:
      - name: train
        num_examples: 35986
      - name: dev
        num_examples: 5125
      - name: test
        num_examples: 10159
  - config_name: Cyberbullying
    splits:
      - name: train
        num_examples: 32551
      - name: dev
        num_examples: 4751
      - name: test
        num_examples: 9473
  - config_name: clef2024-checkthat-lab
    splits:
      - name: train
        num_examples: 825
      - name: dev
        num_examples: 219
      - name: test
        num_examples: 484
  - config_name: SemEval23T3-subtask1
    splits:
      - name: train
        num_examples: 302
      - name: dev
        num_examples: 130
      - name: test
        num_examples: 83
  - config_name: offensive_language_dataset
    splits:
      - name: train
        num_examples: 29216
      - name: dev
        num_examples: 3653
      - name: test
        num_examples: 3653
  - config_name: xlsum
    splits:
      - name: train
        num_examples: 306493
      - name: dev
        num_examples: 11535
      - name: test
        num_examples: 11535
  - config_name: claim-detection
    splits:
      - name: train
        num_examples: 23224
      - name: dev
        num_examples: 5815
      - name: test
        num_examples: 7267
  - config_name: emotion
    splits:
      - name: train
        num_examples: 280551
      - name: dev
        num_examples: 41429
      - name: test
        num_examples: 82454
  - config_name: Politifact
    splits:
      - name: train
        num_examples: 14799
      - name: dev
        num_examples: 2116
      - name: test
        num_examples: 4230
  - config_name: News_dataset
    splits:
      - name: train
        num_examples: 28147
      - name: dev
        num_examples: 4376
      - name: test
        num_examples: 8616
  - config_name: hate-offensive-speech
    splits:
      - name: train
        num_examples: 48944
      - name: dev
        num_examples: 2802
      - name: test
        num_examples: 2799
  - config_name: CNN_News_Articles_2011-2022
    splits:
      - name: train
        num_examples: 32193
      - name: dev
        num_examples: 9663
      - name: test
        num_examples: 5682
  - config_name: CT24_checkworthy
    splits:
      - name: train
        num_examples: 22403
      - name: dev
        num_examples: 318
      - name: test
        num_examples: 1031
  - config_name: News_Category_Dataset
    splits:
      - name: train
        num_examples: 145748
      - name: dev
        num_examples: 20899
      - name: test
        num_examples: 41740
  - config_name: NewsMTSC-dataset
    splits:
      - name: train
        num_examples: 7739
      - name: dev
        num_examples: 320
      - name: test
        num_examples: 747
  - config_name: Offensive_Hateful_Dataset_New
    splits:
      - name: train
        num_examples: 42000
      - name: dev
        num_examples: 5254
      - name: test
        num_examples: 5252
  - config_name: News-Headlines-Dataset-For-Sarcasm-Detection
    splits:
      - name: train
        num_examples: 19965
      - name: dev
        num_examples: 2858
      - name: test
        num_examples: 5719
configs:
  - config_name: QProp
    data_files:
      - split: test
        path: QProp/test.json
      - split: dev
        path: QProp/dev.json
      - split: train
        path: QProp/train.json
  - config_name: Cyberbullying
    data_files:
      - split: test
        path: Cyberbullying/test.json
      - split: dev
        path: Cyberbullying/dev.json
      - split: train
        path: Cyberbullying/train.json
  - config_name: clef2024-checkthat-lab
    data_files:
      - split: test
        path: clef2024-checkthat-lab/test.json
      - split: dev
        path: clef2024-checkthat-lab/dev.json
      - split: train
        path: clef2024-checkthat-lab/train.json
  - config_name: SemEval23T3-subtask1
    data_files:
      - split: test
        path: SemEval23T3-subtask1/test.json
      - split: dev
        path: SemEval23T3-subtask1/dev.json
      - split: train
        path: SemEval23T3-subtask1/train.json
  - config_name: offensive_language_dataset
    data_files:
      - split: test
        path: offensive_language_dataset/test.json
      - split: dev
        path: offensive_language_dataset/dev.json
      - split: train
        path: offensive_language_dataset/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: claim-detection
    data_files:
      - split: test
        path: claim-detection/test.json
      - split: dev
        path: claim-detection/dev.json
      - split: train
        path: claim-detection/train.json
  - config_name: emotion
    data_files:
      - split: test
        path: emotion/test.json
      - split: dev
        path: emotion/dev.json
      - split: train
        path: emotion/train.json
  - config_name: Politifact
    data_files:
      - split: test
        path: Politifact/test.json
      - split: dev
        path: Politifact/dev.json
      - split: train
        path: Politifact/train.json
  - config_name: News_dataset
    data_files:
      - split: test
        path: News_dataset/test.json
      - split: dev
        path: News_dataset/dev.json
      - split: train
        path: News_dataset/train.json
  - config_name: hate-offensive-speech
    data_files:
      - split: test
        path: hate-offensive-speech/test.json
      - split: dev
        path: hate-offensive-speech/dev.json
      - split: train
        path: hate-offensive-speech/train.json
  - config_name: CNN_News_Articles_2011-2022
    data_files:
      - split: test
        path: CNN_News_Articles_2011-2022/test.json
      - split: dev
        path: CNN_News_Articles_2011-2022/dev.json
      - split: train
        path: CNN_News_Articles_2011-2022/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: News_Category_Dataset
    data_files:
      - split: test
        path: News_Category_Dataset/test.json
      - split: dev
        path: News_Category_Dataset/dev.json
      - split: train
        path: News_Category_Dataset/train.json
  - config_name: NewsMTSC-dataset
    data_files:
      - split: test
        path: NewsMTSC-dataset/test.json
      - split: dev
        path: NewsMTSC-dataset/dev.json
      - split: train
        path: NewsMTSC-dataset/train.json
  - config_name: Offensive_Hateful_Dataset_New
    data_files:
      - split: test
        path: Offensive_Hateful_Dataset_New/test.json
      - split: dev
        path: Offensive_Hateful_Dataset_New/dev.json
      - split: train
        path: Offensive_Hateful_Dataset_New/train.json
  - config_name: News-Headlines-Dataset-For-Sarcasm-Detection
    data_files:
      - split: test
        path: News-Headlines-Dataset-For-Sarcasm-Detection/test.json
      - split: dev
        path: News-Headlines-Dataset-For-Sarcasm-Detection/dev.json
      - split: train
        path: News-Headlines-Dataset-For-Sarcasm-Detection/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 19 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)
  • 19 NLP tasks with 52 datasets
  • Optimized for news and social media content analysis

📂 Dataset Overview

English Datasets

Task Dataset # Labels # Train # Test # Dev
Checkworthiness CT24_T1 2 22,403 1,031 318
Claim claim-detection 2 23,224 7,267 5,815
Cyberbullying Cyberbullying 6 32,551 9,473 4,751
Emotion emotion 6 280,551 82,454 41,429
Factuality News_dataset 2 28,147 8,616 4,376
Factuality Politifact 6 14,799 4,230 2,116
News Genre Categorization CNN_News_Articles_2011-2022 6 32,193 5,682 9,663
News Genre Categorization News_Category_Dataset 42 145,748 41,740 20,899
News Genre Categorization SemEval23T3-subtask1 3 302 83 130
Summarization xlsum -- 306,493 11,535 11,535
Offensive Language Offensive_Hateful_Dataset_New 2 42,000 5,252 5,254
Offensive Language offensive_language_dataset 2 29,216 3,653 3,653
Offensive/Hate-Speech hate-offensive-speech 3 48,944 2,799 2,802
Propaganda QProp 2 35,986 10,159 5,125
Sarcasm News-Headlines-Dataset-For-Sarcasm-Detection 2 19,965 5,719 2,858
Sentiment NewsMTSC-dataset 3 7,739 747 320
Subjectivity clef2024-checkthat-lab 2 825 484 219

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.
  • text: A formatted structure including instructions and response for the task in a conversation format between the system, user, and assistant, showing the decision process.

Example entry in JSONL file:

{
    "id": "3fe3eb6a-843e-4a03-b38c-8333c052f4c4",
    "original_id": "nan",
    "input": "You know, I saw a movie - \"Crocodile Dundee.\"",
    "output": "not_checkworthy",
    "dataset": "CT24_checkworthy",
    "task": "Checkworthiness",
    "lang": "en",
    "instructions": "Analyze the given text and label it as 'checkworthy' if it includes a factual statement that is significant or relevant to verify, or 'not_checkworthy' if it's not worth checking. Return only the label without any explanation, justification or additional text.",
    "text": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are a social media expert providing accurate analysis and insights.<|eot_id|><|start_header_id|>user<|end_header_id|>Analyze the given text and label it as 'checkworthy' if it includes a factual statement that is significant or relevant to verify, or 'not_checkworthy' if it's not worth checking. Return only the label without any explanation, justification or additional text.\ninput: You know, I saw a movie - \"Crocodile Dundee.\"\nlabel: <|eot_id|><|start_header_id|>assistant<|end_header_id|>not_checkworthy<|eot_id|><|end_of_text|>"
}

📢 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}
}