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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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language: |
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- hi |
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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: "LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content -- Hindi-Native" |
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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: Sentiment_Analysis |
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splits: |
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- name: train |
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num_examples: 10039 |
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- name: dev |
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num_examples: 1258 |
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- name: test |
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num_examples: 1259 |
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- config_name: MC_Hinglish1 |
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splits: |
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- name: train |
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num_examples: 5177 |
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- name: dev |
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num_examples: 2219 |
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- name: test |
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num_examples: 1000 |
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- config_name: Offensive_Speech_Detection |
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splits: |
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- name: train |
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num_examples: 2172 |
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- name: dev |
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num_examples: 318 |
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- name: test |
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num_examples: 636 |
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- config_name: xlsum |
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splits: |
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- name: train |
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num_examples: 70754 |
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- name: dev |
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num_examples: 8847 |
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- name: test |
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num_examples: 8847 |
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- config_name: Hindi-Hostility-Detection-CONSTRAINT-2021 |
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splits: |
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- name: train |
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num_examples: 5718 |
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- name: dev |
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num_examples: 811 |
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- name: test |
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num_examples: 1651 |
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- config_name: hate-speech-detection |
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splits: |
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- name: train |
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num_examples: 3327 |
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- name: dev |
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num_examples: 476 |
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- name: test |
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num_examples: 951 |
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- config_name: fake-news |
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splits: |
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- name: train |
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num_examples: 8393 |
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- name: dev |
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num_examples: 1417 |
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- name: test |
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num_examples: 2743 |
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- config_name: Natural_Language_Inference |
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splits: |
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- name: train |
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num_examples: 1251 |
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- name: dev |
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num_examples: 537 |
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- name: test |
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num_examples: 447 |
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configs: |
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- config_name: Sentiment_Analysis |
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data_files: |
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- split: test |
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path: Sentiment_Analysis/test.json |
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- split: dev |
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path: Sentiment_Analysis/dev.json |
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- split: train |
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path: Sentiment_Analysis/train.json |
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- config_name: MC_Hinglish1 |
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data_files: |
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- split: test |
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path: MC_Hinglish1/test.json |
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- split: dev |
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path: MC_Hinglish1/dev.json |
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- split: train |
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path: MC_Hinglish1/train.json |
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- config_name: Offensive_Speech_Detection |
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data_files: |
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- split: test |
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path: Offensive_Speech_Detection/test.json |
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- split: dev |
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path: Offensive_Speech_Detection/dev.json |
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- split: train |
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path: Offensive_Speech_Detection/train.json |
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- config_name: xlsum |
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data_files: |
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- split: test |
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path: xlsum/test.json |
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- split: dev |
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path: xlsum/dev.json |
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- split: train |
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path: xlsum/train.json |
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- config_name: Hindi-Hostility-Detection-CONSTRAINT-2021 |
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data_files: |
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- split: test |
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path: Hindi-Hostility-Detection-CONSTRAINT-2021/test.json |
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- split: dev |
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path: Hindi-Hostility-Detection-CONSTRAINT-2021/dev.json |
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- split: train |
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path: Hindi-Hostility-Detection-CONSTRAINT-2021/train.json |
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- config_name: hate-speech-detection |
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data_files: |
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- split: test |
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path: hate-speech-detection/test.json |
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- split: dev |
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path: hate-speech-detection/dev.json |
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- split: train |
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path: hate-speech-detection/train.json |
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- config_name: fake-news |
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data_files: |
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- split: test |
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path: fake-news/test.json |
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- split: dev |
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path: fake-news/dev.json |
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- split: train |
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path: fake-news/train.json |
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- config_name: Natural_Language_Inference |
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data_files: |
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- split: test |
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path: Natural_Language_Inference/test.json |
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- split: dev |
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path: Natural_Language_Inference/dev.json |
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- split: train |
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path: Natural_Language_Inference/train.json |
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--- |
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# LlamaLens: Specialized Multilingual LLM Dataset |
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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. |
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<p align="center"> <img src="https://huggingface.co/datasets/QCRI/LlamaLens-Arabic/resolve/main/capablities_tasks_datasets.png" style="width: 40%;" id="title-icon"> </p> |
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## LlamaLens |
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This repo includes scripts needed to run our full pipeline, including data preprocessing and sampling, instruction dataset creation, model fine-tuning, inference and evaluation. |
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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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### Hindi Datasets |
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| **Task** | **Dataset** | **# Labels** | **# Train** | **# Test** | **# Dev** | |
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| -------------------------- | ----------------------------------------- | ------------ | ----------- | ---------- | --------- | |
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| Cyberbullying | MC-Hinglish1.0 | 7 | 7,400 | 1,000 | 2,119 | |
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| Factuality | fake-news | 2 | 8,393 | 2,743 | 1,417 | |
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| Hate Speech | hate-speech-detection | 2 | 3,327 | 951 | 476 | |
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| Hate Speech | Hindi-Hostility-Detection-CONSTRAINT-2021 | 15 | 5,718 | 1,651 | 811 | |
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| Natural_Language_Inference | Natural_Language_Inference | 2 | 1,251 | 447 | 537 | |
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| Summarization | xlsum | -- | 70,754 | 8,847 | 8,847 | |
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| Offensive Speech | Offensive_Speech_Detection | 3 | 2,172 | 636 | 318 | |
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| Sentiment | Sentiment_Analysis | 3 | 10,039 | 1,259 | 1,258 | |
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--- |
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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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--- |
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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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| Factuality | fake-news | Mi-F1 | -- | 0.759 | 0.994 | 0.993 | -- | |
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| Hate Speech Detection | hate-speech-detection | Mi-F1 | 0.639 | 0.750 | 0.963 | 0.963 | 0.324 | |
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| Hate Speech Detection | Hindi-Hostility-Detection-CONSTRAINT-2021 | W-F1 | 0.841 | 0.469 | 0.753 | 0.753 | -0.088 | |
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| Natural Language Inference | Natural Language Inference | W-F1 | 0.646 | 0.633 | 0.568 | 0.679 | -0.078 | |
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| News Summarization | xlsum | R-2 | 0.136 | 0.078 | 0.171 | 0.170 | 0.035 | |
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| Offensive Language Detection | Offensive Speech Detection | Mi-F1 | 0.723 | 0.621 | 0.862 | 0.865 | 0.139 | |
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| Cyberbullying Detection | MC_Hinglish1 | Acc | 0.609 | 0.233 | 0.625 | 0.627 | 0.016 | |
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| Sentiment Classification | Sentiment Analysis | Acc | 0.697 | 0.552 | 0.647 | 0.654 | -0.050 |
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## File Format |
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Each JSONL file in the dataset follows a structured format with the following fields: |
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- `id`: Unique identifier for each data entry. |
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- `original_id`: Identifier from the original dataset, if available. |
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- `input`: The original text that needs to be analyzed. |
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- `output`: The label assigned to the text after analysis. |
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- `dataset`: Name of the dataset the entry belongs. |
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- `task`: The specific task type. |
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- `lang`: The language of the input text. |
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- `instructions`: A brief set of instructions describing how the text should be labeled. |
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**Example entry in JSONL file:** |
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``` |
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{ |
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"id": "5486ee85-4a70-4b33-8711-fb2a0b6d81e1", |
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"original_id": null, |
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"input": "आप और बाकी सभी मुसलमान समाज के लिए आशीर्वाद हैं.", |
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"output": "not-hateful", |
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"dataset": "hate-speech-detection", |
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"task": "Factuality", |
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"lang": "hi", |
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"instructions": "Classify the given text as either 'not-hateful' or 'hateful'. 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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## Replication Scripts |
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[**LlamaLens GitHub Repository**](https://github.com/firojalam/LlamaLens) |
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## 📢 Citation |
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If you use this dataset, please cite our [paper](https://arxiv.org/pdf/2410.15308): |
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``` |
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@article{kmainasi2024llamalensspecializedmultilingualllm, |
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title={LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content}, |
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author={Mohamed Bayan Kmainasi and Ali Ezzat Shahroor and Maram Hasanain and Sahinur Rahman Laskar and Naeemul Hassan and Firoj Alam}, |
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year={2024}, |
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journal={arXiv preprint arXiv:2410.15308}, |
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volume={}, |
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number={}, |
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pages={}, |
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url={https://arxiv.org/abs/2410.15308}, |
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eprint={2410.15308}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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