---
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
---
# 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 19 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 AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/LlamaLens"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
3. **Prepare the Input:**:
Tokenize your input text:
```python
input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
```
4. **Generate the Output:**:
Generate a response using the model:
```python
output = model.generate(**inputs)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)
```

## 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:

```
   @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}
   }
```