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---
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.
<p align="center"> <img src="./capablities_tasks_datasets.png" style="width: 40%;" id="title-icon"> </p>
## 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 |
## Results
Below, we present the performance of **LlamaLens** in **English** compared to existing SOTA (if available) and the Llama-Instruct baseline, The “Δ” (Delta) column here is
calculated as **(LLamalens – SOTA)**.
| **Task** | **Dataset** | **Metric** | **SOTA** | **Llama-instruct** | **LLamalens** | **Δ** (LLamalens - SOTA) |
|----------------------|---------------------------|-----------:|--------:|--------------------:|--------------:|------------------------------:|
| News Summarization | xlsum | R-2 | 0.152 | 0.074 | 0.141 | -0.011 |
| News Genre | CNN_News_Articles | Acc | 0.940 | 0.644 | 0.915 | -0.025 |
| News Genre | News_Category | Ma-F1 | 0.769 | 0.970 | 0.505 | -0.264 |
| News Genre | SemEval23T3-ST1 | Mi-F1 | 0.815 | 0.687 | 0.241 | -0.574 |
| Subjectivity | CT24_T2 | Ma-F1 | 0.744 | 0.535 | 0.508 | -0.236 |
| Emotion | emotion | Ma-F1 | 0.790 | 0.353 | 0.878 | 0.088 |
| Sarcasm | News-Headlines | Acc | 0.897 | 0.668 | 0.956 | 0.059 |
| Sentiment | NewsMTSC | Ma-F1 | 0.817 | 0.628 | 0.627 | -0.190 |
| Checkworthiness | CT24_T1 | F1_Pos | 0.753 | 0.404 | 0.877 | 0.124 |
| Claim | claim-detection | Mi-F1 | – | 0.545 | 0.915 | – |
| Factuality | News_dataset | Acc | 0.920 | 0.654 | 0.946 | 0.026 |
| Factuality | Politifact | W-F1 | 0.490 | 0.121 | 0.290 | -0.200 |
| Propaganda | QProp | Ma-F1 | 0.667 | 0.759 | 0.851 | 0.184 |
| Cyberbullying | Cyberbullying | Acc | 0.907 | 0.175 | 0.847 | -0.060 |
| Offensive | Offensive_Hateful | Mi-F1 | – | 0.692 | 0.805 | – |
| Offensive | offensive_language | Mi-F1 | 0.994 | 0.646 | 0.884 | -0.110 |
| Offensive & Hate | hate-offensive-speech | Acc | 0.945 | 0.602 | 0.924 | -0.021 |
## 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|>"
}
```
## Model
[**LlamaLens on Hugging Face**](https://huggingface.co/QCRI/LlamaLens)
## Replication Scripts
[**LlamaLens GitHub Repository**](https://github.com/firojalam/LlamaLens)
## 📢 Citation
If you use this dataset, please cite our [paper](https://arxiv.org/pdf/2410.15308):
```
@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}
}
```
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