--- 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. <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) - 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 | --- ## Results Below, we present the performance of **LlamaLens** in **Hindi** compared to existing SOTA (if available) and the Llama-Instruct baseline, The “Delta” column here is calculated as **(LLamalens – SOTA)**. --- | **Task** | **Dataset** | **Metric** | **SOTA** | **Llama-instruct** | **LLamalens** | **Delta** (LLamalens - SOTA) | |------------------------|------------------------|-----------:|--------:|--------------------:|--------------:|------------------------------:| | NLI | NLI_dataset | W-F1 | 0.646 | 0.633 | 0.655 | 0.009 | | News Summarization | xlsum | R-2 | 0.136 | 0.078 | 0.117 | -0.019 | | Sentiment | Sentiment Analysis | Acc | 0.697 | 0.552 | 0.669 | -0.028 | | Factuality | fake-news | Mi-F1 | – | 0.759 | 0.713 | – | | Hate Speech | hate-speech-detection | Mi-F1 | 0.639 | 0.750 | 0.994 | 0.355 | | Hate Speech | Hindi-Hostility | W-F1 | 0.841 | 0.469 | 0.720 | -0.121 | | Offensive | Offensive Speech | Mi-F1 | 0.723 | 0.621 | 0.847 | 0.124 | | Cyberbullying | MC_Hinglish1 | Acc | 0.609 | 0.233 | 0.587 | -0.022 | ## 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." } ``` ## 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} } ```