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---
license: cc-by-nc-sa-4.0
datasets:
- QCRI/LlamaLens-English
- QCRI/LlamaLens-Arabic
- QCRI/LlamaLens-Hindi
language:
- ar
- en
- hi
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
tags:
- Social-Media
- Hate-Speech
- Summarization
- offensive-language
- News-Genre
metrics:
- accuracy
- f1
- rouge
---
# LlamaLens: Specialized Multilingual LLM forAnalyzing News and Social Media Content
## 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">
<picture>
<img width="352" alt="capablities_tasks_datasets" src="./llamalens-avatar.png">
</picture>
</p>
## Dataset
The model was trained on the [LlamaLens dataset](https://huggingface.co/collections/QCRI/llamalens-672f7e0604a0498c6a2f0fe9).
## To Replicate the Experiments
The code to replicate the experiments is available on [GitHub](https://github.com/firojalam/LlamaLens).
## Model Inference
To utilize the LlamaLens model for inference, follow these steps:
1. **Install the Required Libraries**:
Ensure you have the necessary libraries installed. You can do this using pip:
```bash
pip install transformers torch
```
2. **Load the Model and Tokenizer:**:
Use the transformers library to load the LlamaLens model and its tokenizer:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Define model path
MODEL_PATH = "QCRI/LlamaLens"
# Load model and tokenizer
device_map = "auto"
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
```
3. **Prepare the Input:**:
Tokenize your input text:
```python
# Define task and input text
task = "classification" # Change to "summarization" for summarization tasks
instruction = (
"Analyze the text and indicate if it shows an emotion, then label it as joy, love, fear,"
" anger, sadness, or surprise. Return only the label without any explanation, justification, or additional text."
)
input_text = "I am not creating anything I feel satisfied with."
output_prefix = "Summary: " if task == "summarization" else "Label: "
# Define messages for chat-based prompt format
messages = [
{"role": "system", "content": "You are a social media expert providing accurate analysis and insights."},
{"role": "user", "content": f"{instruction}\nInput: {input_text}"},
{"role": "assistant", "content": output_prefix}
]
# Tokenize input
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=False,
continue_final_message=True,
tokenize=True,
padding=True,
return_tensors="pt"
).to(model.device)
```
4. **Generate the Output:**:
Generate a response using the model:
```python
# Generate response
outputs = model.generate(
input_ids,
max_new_tokens=128,
do_sample=False,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
temperature=0.001
)
# Decode and print response
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```
## 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).
---
## Arabic
| **Task** | **Dataset** | **Metric** | **SOTA** | **Base** | **L-Lens-Eng** | **L-Lens-Native** | **Δ (L-Lens (Eng) - SOTA)** |
|:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
| Attentionworthiness Detection | CT22Attentionworthy | W-F1 | 0.412 | 0.158 | 0.425 | 0.454 | 0.013 |
| Checkworthiness Detection | CT24_checkworthy | F1_Pos | 0.569 | 0.610 | 0.502 | 0.509 | -0.067 |
| Claim Detection | CT22Claim | Acc | 0.703 | 0.581 | 0.734 | 0.756 | 0.031 |
| Cyberbullying Detection | ArCyc_CB | Acc | 0.863 | 0.766 | 0.870 | 0.833 | 0.007 |
| Emotion Detection | Emotional-Tone | W-F1 | 0.658 | 0.358 | 0.705 | 0.736 | 0.047 |
| Emotion Detection | NewsHeadline | Acc | 1.000 | 0.406 | 0.480 | 0.458 | -0.520 |
| Factuality | Arafacts | Mi-F1 | 0.850 | 0.210 | 0.771 | 0.738 | -0.079 |
| Factuality | COVID19Factuality | W-F1 | 0.831 | 0.492 | 0.800 | 0.840 | -0.031 |
| Harmfulness Detection | CT22Harmful | F1_Pos | 0.557 | 0.507 | 0.523 | 0.535 | -0.034 |
| Hate Speech Detection | annotated-hatetweets-4-classes | W-F1 | 0.630 | 0.257 | 0.526 | 0.517 | -0.104 |
| Hate Speech Detection | OSACT4SubtaskB | Mi-F1 | 0.950 | 0.819 | 0.955 | 0.955 | 0.005 |
| News Categorization | ASND | Ma-F1 | 0.770 | 0.587 | 0.919 | 0.929 | 0.149 |
| News Categorization | SANADAkhbarona-news-categorization | Acc | 0.940 | 0.784 | 0.954 | 0.953 | 0.014 |
| News Categorization | SANADAlArabiya-news-categorization | Acc | 0.974 | 0.893 | 0.987 | 0.985 | 0.013 |
| News Categorization | SANADAlkhaleej-news-categorization | Acc | 0.986 | 0.865 | 0.984 | 0.982 | -0.002 |
| News Categorization | UltimateDataset | Ma-F1 | 0.970 | 0.376 | 0.865 | 0.880 | -0.105 |
| News Credibility | NewsCredibilityDataset | Acc | 0.899 | 0.455 | 0.935 | 0.933 | 0.036 |
| News Summarization | xlsum | R-2 | 0.137 | 0.034 | 0.129 | 0.130 | -0.009 |
| Offensive Language Detection | ArCyc_OFF | Ma-F1 | 0.878 | 0.489 | 0.877 | 0.879 | -0.001 |
| Offensive Language Detection | OSACT4SubtaskA | Ma-F1 | 0.905 | 0.782 | 0.896 | 0.882 | -0.009 |
| Propaganda Detection | ArPro | Mi-F1 | 0.767 | 0.597 | 0.747 | 0.731 | -0.020 |
| Sarcasm Detection | ArSarcasm-v2 | F1_Pos | 0.584 | 0.477 | 0.520 | 0.542 | -0.064 |
| Sentiment Classification | ar_reviews_100k | F1_Pos | -- | 0.681 | 0.785 | 0.779 | -- |
| Sentiment Classification | ArSAS | Acc | 0.920 | 0.603 | 0.800 | 0.804 | -0.120 |
| Stance Detection | stance | Ma-F1 | 0.767 | 0.608 | 0.926 | 0.881 | 0.159 |
| Stance Detection | Mawqif-Arabic-Stance-main | Ma-F1 | 0.789 | 0.764 | 0.853 | 0.826 | 0.065 |
| Subjectivity Detection | ThatiAR | f1_pos | 0.800 | 0.562 | 0.441 | 0.383 | -0.359 |
---
## English
| **Task** | **Dataset** | **Metric** | **SOTA** | **Base** | **L-Lens-Eng** | **L-Lens-Native** | **Δ (L-Lens (Eng) - SOTA)** |
|:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
| Checkworthiness Detection | CT24_checkworthy | f1_pos | 0.753 | 0.404 | 0.942 | 0.942 | 0.189 |
| Claim Detection | claim-detection | Mi-F1 | -- | 0.545 | 0.864 | 0.889 | -- |
| Cyberbullying Detection | Cyberbullying | Acc | 0.907 | 0.175 | 0.836 | 0.855 | -0.071 |
| Emotion Detection | emotion | Ma-F1 | 0.790 | 0.353 | 0.803 | 0.808 | 0.013 |
| Factuality | News_dataset | Acc | 0.920 | 0.654 | 1.000 | 1.000 | 0.080 |
| Factuality | Politifact | W-F1 | 0.490 | 0.121 | 0.287 | 0.311 | -0.203 |
| News Categorization | CNN_News_Articles_2011-2022 | Acc | 0.940 | 0.644 | 0.970 | 0.970 | 0.030 |
| News Categorization | News_Category_Dataset | Ma-F1 | 0.769 | 0.970 | 0.824 | 0.520 | 0.055 |
| News Genre Categorisation | SemEval23T3-subtask1 | Mi-F1 | 0.815 | 0.687 | 0.241 | 0.253 | -0.574 |
| News Summarization | xlsum | R-2 | 0.152 | 0.074 | 0.182 | 0.181 | 0.030 |
| Offensive Language Detection | Offensive_Hateful_Dataset_New | Mi-F1 | -- | 0.692 | 0.814 | 0.813 | -- |
| Offensive Language Detection | offensive_language_dataset | Mi-F1 | 0.994 | 0.646 | 0.899 | 0.893 | -0.095 |
| Offensive Language and Hate Speech | hate-offensive-speech | Acc | 0.945 | 0.602 | 0.931 | 0.935 | -0.014 |
| Propaganda Detection | QProp | Ma-F1 | 0.667 | 0.759 | 0.963 | 0.973 | 0.296 |
| Sarcasm Detection | News-Headlines-Dataset-For-Sarcasm-Detection | Acc | 0.897 | 0.668 | 0.936 | 0.947 | 0.039 |
| Sentiment Classification | NewsMTSC-dataset | Ma-F1 | 0.817 | 0.628 | 0.751 | 0.748 | -0.066 |
| Subjectivity Detection | clef2024-checkthat-lab | Ma-F1 | 0.744 | 0.535 | 0.642 | 0.628 | -0.102 |
|
---
## Hindi
| **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
## Paper
For an in-depth understanding, refer to our paper: [**LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content**](https://arxiv.org/pdf/2410.15308).
# License
This model is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
# Citation
Please cite [our paper](https://arxiv.org/pdf/2410.15308) when using this model:
```
@inproceedings{kmainasi-etal-2025-llamalens,
title = "{L}lama{L}ens: Specialized Multilingual {LLM} for Analyzing News and Social Media Content",
author = "Kmainasi, Mohamed Bayan and
Shahroor, Ali Ezzat and
Hasanain, Maram and
Laskar, Sahinur Rahman and
Hassan, Naeemul and
Alam, Firoj",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.313/",
doi = "10.18653/v1/2025.findings-naacl.313",
pages = "5642--5664",
ISBN = "979-8-89176-195-7",
}
```

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special_tokens_map.json Normal file
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{
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}

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tokenizer.json Normal file
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tokenizer_config.json Normal file
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