---
license: cc-by-nc-sa-4.0
task_categories:
  - text-classification
language:
  - hi
tags:
  - Social Media
  - News Media
  - Sentiment
  - Stance
  - Emotion
pretty_name: "LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content -- Hindi"
size_categories:
  - 10K<n<100K
dataset_info:
  - config_name: Sentiment_Analysis
    splits:
      - name: train
        num_examples: 10039
      - name: dev
        num_examples: 1258
      - name: test
        num_examples: 1259
  - config_name: MC_Hinglish1
    splits:
      - name: train
        num_examples: 5177
      - name: dev
        num_examples: 2219
      - name: test
        num_examples: 1000
  - config_name: Offensive_Speech_Detection
    splits:
      - name: train
        num_examples: 2172
      - name: dev
        num_examples: 318
      - name: test
        num_examples: 636
  - config_name: xlsum
    splits:
      - name: train
        num_examples: 70754
      - name: dev
        num_examples: 8847
      - name: test
        num_examples: 8847
  - config_name: Hindi-Hostility-Detection-CONSTRAINT-2021
    splits:
      - name: train
        num_examples: 5718
      - name: dev
        num_examples: 811
      - name: test
        num_examples: 1651
  - config_name: hate-speech-detection
    splits:
      - name: train
        num_examples: 3327
      - name: dev
        num_examples: 476
      - name: test
        num_examples: 951
  - config_name: fake-news
    splits:
      - name: train
        num_examples: 8393
      - name: dev
        num_examples: 1417
      - name: test
        num_examples: 2743
  - config_name: Natural_Language_Inference
    splits:
      - name: train
        num_examples: 1251
      - name: dev
        num_examples: 537
      - name: test
        num_examples: 447
configs:
  - config_name: Sentiment_Analysis
    data_files:
      - split: test
        path: Sentiment_Analysis/test.json
      - split: dev
        path: Sentiment_Analysis/dev.json
      - split: train
        path: Sentiment_Analysis/train.json
  - config_name: MC_Hinglish1
    data_files:
      - split: test
        path: MC_Hinglish1/test.json
      - split: dev
        path: MC_Hinglish1/dev.json
      - split: train
        path: MC_Hinglish1/train.json
  - config_name: Offensive_Speech_Detection
    data_files:
      - split: test
        path: Offensive_Speech_Detection/test.json
      - split: dev
        path: Offensive_Speech_Detection/dev.json
      - split: train
        path: Offensive_Speech_Detection/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: Hindi-Hostility-Detection-CONSTRAINT-2021
    data_files:
      - split: test
        path: Hindi-Hostility-Detection-CONSTRAINT-2021/test.json
      - split: dev
        path: Hindi-Hostility-Detection-CONSTRAINT-2021/dev.json
      - split: train
        path: Hindi-Hostility-Detection-CONSTRAINT-2021/train.json
  - config_name: hate-speech-detection
    data_files:
      - split: test
        path: hate-speech-detection/test.json
      - split: dev
        path: hate-speech-detection/dev.json
      - split: train
        path: hate-speech-detection/train.json
  - config_name: fake-news
    data_files:
      - split: test
        path: fake-news/test.json
      - split: dev
        path: fake-news/dev.json
      - split: train
        path: fake-news/train.json
  - config_name: Natural_Language_Inference
    data_files:
      - split: test
        path: Natural_Language_Inference/test.json
      - split: dev
        path: Natural_Language_Inference/dev.json
      - split: train
        path: Natural_Language_Inference/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.

<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>

## 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

## 📂 Dataset Overview

### Hindi Datasets

| **Task**                   | **Dataset**                               | **# Labels** | **# Train** | **# Test** | **# Dev** |
| -------------------------- | ----------------------------------------- | ------------ | ----------- | ---------- | --------- |
| Cyberbullying              | MC-Hinglish1.0                            | 7            | 7,400       | 1,000      | 2,119     |
| Factuality                 | fake-news                                 | 2            | 8,393       | 2,743      | 1,417     |
| Hate Speech                | hate-speech-detection                     | 2            | 3,327       | 951        | 476       |
| Hate Speech                | Hindi-Hostility-Detection-CONSTRAINT-2021 | 15           | 5,718       | 1,651      | 811       |
| Natural_Language_Inference | Natural_Language_Inference                | 2            | 1,251       | 447        | 537       |
| Summarization              | xlsum                                     | --           | 70,754      | 8,847      | 8,847     |
| Offensive Speech           | Offensive_Speech_Detection                | 3            | 2,172       | 636        | 318       |
| Sentiment                  | Sentiment_Analysis                        | 3            | 10,039      | 1,259      | 1,258     |

---

## 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 **Δ** (Delta) column indicates the difference between LlamaLens and the SOTA performance, calculated as (LlamaLens – SOTA).


---
|              **Task**              |                  **Dataset**                 | **Metric** | **SOTA** | **Base** | **L-Lens-Eng** | **L-Lens-Native** | **Δ (L-Lens (Eng) - SOTA)** |
|:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
|             Factuality             |                   fake-news                  |    Mi-F1   |    --    |         0.759         |         0.994         |         0.993        |            --            |
|        Hate Speech Detection       |             hate-speech-detection            |    Mi-F1   |   0.639  |         0.750         |         0.963         |         0.963        |           0.324          |
|        Hate Speech Detection       |   Hindi-Hostility-Detection-CONSTRAINT-2021  |    W-F1    |   0.841  |         0.469         |         0.753         |         0.753        |          -0.088          |
|     Natural Language Inference     |          Natural Language Inference          |    W-F1    |   0.646  |         0.633         |         0.568         |         0.679        |          -0.078          |
|         News Summarization         |                     xlsum                    |     R-2    |   0.136  |         0.078         |         0.171         |         0.170        |           0.035          |
|    Offensive Language Detection    |          Offensive Speech Detection          |    Mi-F1   |   0.723  |         0.621         |         0.862         |         0.865        |           0.139          |
|       Cyberbullying Detection      |                 MC_Hinglish1                 |     Acc    |   0.609  |         0.233         |         0.625         |         0.627        |           0.016          |
|      Sentiment Classification      |              Sentiment Analysis              |     Acc    |   0.697  |         0.552         |         0.647         |         0.654        |          -0.050          


## 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": "5486ee85-4a70-4b33-8711-fb2a0b6d81e1",
    "original_id": null,
    "input": "आप और बाकी सभी मुसलमान समाज के लिए आशीर्वाद हैं.",
    "output": "not-hateful",
    "dataset": "hate-speech-detection",
    "task": "Factuality",
    "lang": "hi",
    "instructions": "Classify the given text as either 'not-hateful' or 'hateful'. Return only the label without any explanation, justification, or additional 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}
}
```