readme_hindi.md

metadata

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 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

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

---

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": "2b1878df-5a4f-4f74-bcd8-e38e1c3c7cf6",
        "original_id": null,
        "input": "sub गंदा है पर धंधा है ये . .",
        "output": "neutral",
        "dataset": "Sentiment Analysis",
        "task": "Sentiment",
        "lang": "hi",
        "instruction": "Identify the sentiment in the text and label it as positive, negative, or neutral. Return only the label without any explanation, justification or additional 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}
}