|
--- |
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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" |
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size_categories: |
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- 10K<n<100K |
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dataset_info: |
|
- config_name: Sentiment Analysis |
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splits: |
|
- name: train |
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num_examples: 10039 |
|
- name: dev |
|
num_examples: 1258 |
|
- name: test |
|
num_examples: 1259 |
|
- config_name: MC_Hinglish1 |
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splits: |
|
- name: train |
|
num_examples: 5177 |
|
- name: dev |
|
num_examples: 2219 |
|
- name: test |
|
num_examples: 1000 |
|
- config_name: Offensive Speech Detection |
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splits: |
|
- name: train |
|
num_examples: 2172 |
|
- name: dev |
|
num_examples: 318 |
|
- name: test |
|
num_examples: 636 |
|
- config_name: xlsum |
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splits: |
|
- name: train |
|
num_examples: 70754 |
|
- name: dev |
|
num_examples: 8847 |
|
- name: test |
|
num_examples: 8847 |
|
- config_name: Hindi-Hostility-Detection-CONSTRAINT-2021 |
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splits: |
|
- name: train |
|
num_examples: 5718 |
|
- name: dev |
|
num_examples: 811 |
|
- name: test |
|
num_examples: 1651 |
|
- config_name: hate-speech-detection |
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splits: |
|
- name: train |
|
num_examples: 3327 |
|
- name: dev |
|
num_examples: 476 |
|
- name: test |
|
num_examples: 951 |
|
- config_name: fake-news |
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splits: |
|
- name: train |
|
num_examples: 8393 |
|
- name: dev |
|
num_examples: 1417 |
|
- name: test |
|
num_examples: 2743 |
|
- config_name: Natural Language Inference |
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splits: |
|
- name: train |
|
num_examples: 1251 |
|
- name: dev |
|
num_examples: 537 |
|
- name: test |
|
num_examples: 447 |
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configs: |
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- config_name: Sentiment Analysis |
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data_files: |
|
- split: test |
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path: Sentiment Analysis/test.json |
|
- split: dev |
|
path: Sentiment Analysis/dev.json |
|
- 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: |
|
- 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) |
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- 19 NLP tasks with 52 datasets |
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- 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. |
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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. |
|
- `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. |
|
- `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](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} |
|
} |
|
``` |
|
|
