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metadata
dataset_info:
  features:
    - name: image
      dtype: image
    - name: Text
      dtype: string
    - name: Label
      dtype: string
    - name: Paradigm
      dtype: string
    - name: Incident_based
      dtype: string
    - name: Humor
      dtype: string
    - name: Metaphor
      dtype: string
    - name: Metaphor Object
      dtype: string
    - name: Metaphor Explanation
      dtype: string
    - name: Misogynistic intensity
      dtype: string
  splits:
    - name: train
      num_bytes: 179710931
      num_examples: 1500
    - name: test
      num_bytes: 60020368
      num_examples: 500
  download_size: 242960052
  dataset_size: 239731299
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

BANMIME:Misogyny Detection with Metaphor Explanation on Bangla Memes

anthology code Kaggle

Md. Ayon Mia*, AKM Moshiur Rahman*, Khadiza Sultana Sayma*, Md Fahim*, Md Tahmid Hasan Fuad, Muhammad Ibrahim Khan, and AKM Mahbubur Rahman.

Dataset Overview

  • Misogyny detection (binary) and category identification (multi-class) dataset on 2,000 Bangla meme samples.
  • Four misogyny categories: Stereotype (30.6%), Objectification (25.9%), Shaming (38.0%), and Violence (5.4%).
  • Sourced from 3 social media platforms: Facebook (1,300), Instagram (450), and Reddit (250).
  • Each sample includes misogyny labels, humor types, metaphor localization, and detailed human-written explanations.
  • High inter-annotator agreement with Cohen's κ = 0.74.

Data Format

Column Title Description
meme_id Unique identifier for each meme entry.
image_path Path to the meme image file.
text_content Extracted text content from the meme.
misogyny_label Primary misogyny category (Stereotype / Objectification / Shaming / Violence).
humor_type Type of humor used (Mockery / Sarcastic / Ironic / Satirical / Other).
metaphor_localization Location of metaphor (Text / Image / Both).
metaphor_object Object used in metaphorical expression.
misogyny_intensity Intensity level (High / Moderate / Low).
meme_template Template category (Troll Face / Wojak / Doge / etc).
explanation Detailed human-written explanation of misogynistic content.

🚀 Getting Started

To run the code, please check the scripts in the Misogyny_Code/Evaluate and Misogyny_Code/LLaMA-Factory folders.

📊 Vision-Language Model Performance

We evaluate baselines using both open-source and closed-source vision-language models across different prompting strategies and fine-tuning approaches.

Zero-Shot Prompt

Models Sham Stereo Obj Vio Avg BScore LAVE Avg Expl
Closed Source VLMs
Gemini2.0 Flash 34.97 52.55 65.77 72.41 56.43 86.70 35.00 60.85
GPT-4o-mini 26.26 61.33 60.33 77.42 56.34 87.26 27.20 57.23
Open Source VLMs
Llama-3.2V 11B 12.31 18.12 42.50 16.13 22.27 82.82 0.35 41.59
Gemma-3-12B 52.02 22.67 48.76 67.74 47.80 83.54 8.00 45.77
Qwen2.5-VL 7B 22.73 41.33 57.02 35.48 39.14 78.60 0.60 39.60
Phi-3.5 23.23 67.33 25.62 6.45 30.66 82.30 0.23 41.27
Phi-4 49.49 28.86 20.83 16.13 28.83 81.63 0.20 40.92
LLaVA-1.5 7B 16.92 39.60 27.50 19.35 25.84 82.40 0.22 41.31
LLaVA-NeXT 7B 29.59 25.68 16.53 16.67 22.12 79.25 0.45 39.85

CoT Prompt

Models Sham Stereo Obj Vio Avg BScore LAVE Avg Expl
Closed Source VLMs
Gemini2.0 Flash 48.11 64.23 47.75 55.17 53.82 86.96 31.60 59.28
GPT-4o-mini 22.54 80.69 44.55 33.33 45.28 87.20 22.94 55.07
Open Source VLMs
Llama-3.2V 11B 11.68 35.33 16.67 3.33 16.75 83.53 1.73 42.63
Gemma-3-12B 75.25 11.33 38.02 41.94 41.64 84.49 7.36 45.93
Qwen2.5-VL 7B 24.24 61.74 28.33 16.13 32.61 84.56 5.13 44.85
Phi-4 10.71 59.46 14.17 2.45 22.09 84.41 1.29 42.85
LLaVA-1.5 7B 7.61 57.72 10.08 6.45 20.47 84.34 0.27 42.31
LLaVA-NeXT 7B 8.67 43.62 11.57 6.45 17.58 84.23 2.16 43.20

LoRA (CoT) Fine-Tuning

Models Sham Stereo Obj Vio Avg BScore LAVE Avg Expl
Llama-3.2V 11B 32.83 65.33 52.89 48.39 49.86 86.01 8.00 47.01
Gemma-3-12B 16.16 76.67 49.59 64.52 51.74 86.16 13.80 49.98
Qwen2.5-VL 7B 44.95 59.33 48.76 51.61 51.16 85.36 5.00 45.18
LLaVA-1.5 7B 24.62 48.33 52.98 54.26 45.05 85.19 4.80 44.20
Paligemma-2-10B 8.08 61.33 42.98 61.29 43.42 81.80 2.00 41.90

🔧 Fine-Tuning Strategies

We propose three distinct fine-tuning approaches:

  • Standard LoRA Fine-Tuning: Basic fine-tuning with LoRA adaptation
  • Augmented Direct Prompt Fine-Tuning: Enhanced training with data augmentation
  • Chain-of-Thought Fine-Tuning: Structured reasoning supervision for improved metaphor understanding

Comparative Performance

Models Sham Stereo Obj Vio Avg
LLaMa-3.2V 11B
LoRAaug 0.5 99.3 0.0 3.2 25.9
LoRAstd 11.6 61.3 49.6 61.3 46.0
LoRACoT 32.8 65.3 52.9 48.4 49.9
Gemma-3 12B
LoRAaug 8.1 98.0 7.4 9.7 30.8
LoRAstd 10.1 70.7 49.6 48.4 44.7
LoRACoT 16.2 76.7 49.6 64.5 51.7
Qwen2.5-VL 7B
LoRAaug 49.0 58.7 5.8 22.6 34.0
LoRAstd 47.0 59.0 27.3 37.1 42.6
LoRACoT 45.0 59.3 48.8 51.6 51.2
LLaVa-1.5 7B
LoRAaug 13.1 84.0 0.0 3.2 25.1
LoRAstd 8.1 46.7 38.8 48.4 35.5
LoRACoT 24.6 48.3 53.0 54.3 45.1
Paligemma-2 10B
LoRAaug 38.9 18.0 24.8 16.1 24.4
LoRAstd 9.6 39.3 39.7 58.1 36.7
LoRACoT 8.1 61.3 43.0 61.3 43.4

🔑 Key Findings

  • Chain-of-Thought prompting and fine-tuning improves both classification and explanation quality.
  • Closed-source models outperform open-source models in both classification and explanation tasks.
  • Metaphor interpretation remains challenging for current VLMs, especially in cultural contexts
  • Open-source VLMs struggle with visual metaphors, with performance dropping further when generating explanations.