Add paper link and Github link to LlamaLens dataset card
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by
nielsr
HF staff
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README.md
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---
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license: cc-by-nc-sa-4.0
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task_categories:
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- text-classification
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- en
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tags:
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- Social Media
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- News Media
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- Sentiment
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- Stance
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- Emotion
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pretty_name: >-
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LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media
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Content -- English
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size_categories:
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- 10K<n<100K
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dataset_info:
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- config_name: QProp
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splits:
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# LlamaLens: Specialized Multilingual LLM Dataset
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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.
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<p align="center"> <img src="./capablities_tasks_datasets.png" style="width: 40%;" id="title-icon"> </p>
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##
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This
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### Features
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- Multilingual support (Arabic, English, Hindi)
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- 18 NLP tasks with 52 datasets
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- Optimized for news and social media content analysis
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## 📂 Dataset Overview
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### English Datasets
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| **Task** | **Dataset** | **# Labels** | **# Train** | **# Test** | **# Dev** |
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|---------------------------|------------------------------|--------------|-------------|------------|-----------|
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| Checkworthiness | CT24_T1 | 2 | 22,403 | 1,031 | 318 |
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| Claim | claim-detection | 2 | 23,224 | 7,267 | 5,815 |
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| Cyberbullying | Cyberbullying | 6 | 32,551 | 9,473 | 4,751 |
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| Emotion | emotion | 6 | 280,551 | 82,454 | 41,429 |
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| Factuality | News_dataset | 2 | 28,147 | 8,616 | 4,376 |
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| Factuality | Politifact | 6 | 14,799 | 4,230 | 2,116 |
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| News Genre Categorization | CNN_News_Articles_2011-2022 | 6 | 32,193 | 5,682 | 9,663 |
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| News Genre Categorization | News_Category_Dataset | 42 | 145,748 | 41,740 | 20,899 |
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| News Genre Categorization | SemEval23T3-subtask1 | 3 | 302 | 83 | 130 |
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| Summarization | xlsum | -- | 306,493 | 11,535 | 11,535 |
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| Offensive Language | Offensive_Hateful_Dataset_New | 2 | 42,000 | 5,252 | 5,254 |
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| Offensive Language | offensive_language_dataset | 2 | 29,216 | 3,653 | 3,653 |
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| Offensive/Hate-Speech | hate-offensive-speech | 3 | 48,944 | 2,799 | 2,802 |
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| Propaganda | QProp | 2 | 35,986 | 10,159 | 5,125 |
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| Sarcasm | News-Headlines-Dataset-For-Sarcasm-Detection | 2 | 19,965 | 5,719 | 2,858 |
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| Sentiment | NewsMTSC-dataset | 3 | 7,739 | 747 | 320 |
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| Subjectivity | clef2024-checkthat-lab | 2 | 825 | 484 | 219 |
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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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| Checkworthiness Detection | CT24_checkworthy | f1_pos | 0.753 | 0.404 | 0.942 | 0.942 | 0.189 |
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| Claim Detection | claim-detection | Mi-F1 | -- | 0.545 | 0.864 | 0.889 | -- |
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| Cyberbullying Detection | Cyberbullying | Acc | 0.907 | 0.175 | 0.836 | 0.855 | -0.071 |
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| Emotion Detection | emotion | Ma-F1 | 0.790 | 0.353 | 0.803 | 0.808 | 0.013 |
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| Factuality | News_dataset | Acc | 0.920 | 0.654 | 1.000 | 1.000 | 0.080 |
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| Factuality | Politifact | W-F1 | 0.490 | 0.121 | 0.287 | 0.311 | -0.203 |
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| News Categorization | CNN_News_Articles_2011-2022 | Acc | 0.940 | 0.644 | 0.970 | 0.970 | 0.030 |
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| News Categorization | News_Category_Dataset | Ma-F1 | 0.769 | 0.970 | 0.824 | 0.520 | 0.055 |
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| News Genre Categorisation | SemEval23T3-subtask1 | Mi-F1 | 0.815 | 0.687 | 0.241 | 0.253 | -0.574 |
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| News Summarization | xlsum | R-2 | 0.152 | 0.074 | 0.182 | 0.181 | 0.030 |
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| Offensive Language Detection | Offensive_Hateful_Dataset_New | Mi-F1 | -- | 0.692 | 0.814 | 0.813 | -- |
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| Offensive Language Detection | offensive_language_dataset | Mi-F1 | 0.994 | 0.646 | 0.899 | 0.893 | -0.095 |
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| Offensive Language and Hate Speech | hate-offensive-speech | Acc | 0.945 | 0.602 | 0.931 | 0.935 | -0.014 |
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| Propaganda Detection | QProp | Ma-F1 | 0.667 | 0.759 | 0.963 | 0.973 | 0.296 |
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| Sarcasm Detection | News-Headlines-Dataset-For-Sarcasm-Detection | Acc | 0.897 | 0.668 | 0.936 | 0.947 | 0.039 |
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| Sentiment Classification | NewsMTSC-dataset | Ma-F1 | 0.817 | 0.628 | 0.751 | 0.748 | -0.066 |
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| Subjectivity Detection | clef2024-checkthat-lab | Ma-F1 | 0.744 | 0.535 | 0.642 | 0.628 | -0.102 |
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## File Format
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"lang": "en",
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"instructions": "Identify if the given text expresses an emotion and specify whether it is joy, love, fear, anger, sadness, or surprise. Return only the label without any explanation, justification, or additional text."
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}
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```
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## Model
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[**LlamaLens on Hugging Face**](https://huggingface.co/QCRI/LlamaLens)
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##
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## 📢 Citation
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---
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language:
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- en
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license: cc-by-nc-sa-4.0
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size_categories:
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- 10K<n<100K
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task_categories:
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- text-classification
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pretty_name: 'LlamaLens English Dataset'
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tags:
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- Social Media
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- News Media
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- Sentiment
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- Stance
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- Emotion
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dataset_info:
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- config_name: QProp
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splits:
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# LlamaLens: Specialized Multilingual LLM Dataset
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This dataset supports the research presented in the paper [LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content](https://huggingface.co/papers/2410.15308).
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## Overview
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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. This repository contains the English-language portion of the data.
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<p align="center"> <img src="./capablities_tasks_datasets.png" style="width: 40%;" id="title-icon"> </p>
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## Dataset Details
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This dataset comprises various sub-datasets focusing on different text classification tasks related to news and social media analysis. A detailed breakdown of the datasets and their statistics is provided in the metadata section above.
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## File Format
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"lang": "en",
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"instructions": "Identify if the given text expresses an emotion and specify whether it is joy, love, fear, anger, sadness, or surprise. Return only the label without any explanation, justification, or additional text."
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}
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```
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## Model & Code
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- **LlamaLens Model on Hugging Face:** [https://huggingface.co/QCRI/LlamaLens](https://huggingface.co/QCRI/LlamaLens)
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- **LlamaLens GitHub Repository:** [https://github.com/firojalam/LlamaLens](https://github.com/firojalam/LlamaLens)
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## 📢 Citation
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