File size: 16,732 Bytes
32c6b56 f85b1bf 998f997 c244610 998f997 de095e9 998f997 0bfcf10 998f997 3676651 1dc55cd 3676651 1dc55cd 67a2b6c 1dc55cd 67a2b6c 1dc55cd 67a2b6c 3676651 1dc55cd 3676651 4bf4f99 3adbd21 4bf4f99 a397f21 4bf4f99 3adbd21 c244610 4bf4f99 031534e c244610 4bf4f99 031534e c244610 4bf4f99 3676651 998f997 f85b1bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
---
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:
```
@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}
}
``` |