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language:
- en
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Source: CrisisMMD dataset (Alam et al., 2017)

✅Original Labels (8 classes from annotations):

Infrastructure and utility damage

Vehicle damage

Rescue, volunteering, or donation efforts

Affected individuals

Injured or dead people

Missing or found people

Other relevant information

Not humanitarian

✅Label Preprocessing (Class Merging):

Vehicle damage merged into Infrastructure and utility damage

Missing or found people merged into Affected individuals

Not humanitarian retained as a separate class

Removed very low-frequency categories (e.g., "Missing or found people" as a separate class)

✅Final Label Set (5 classes total):

Infrastructure and utility damage

Rescue, volunteering, or donation efforts

Affected individuals

Injured or dead people

Not humanitarian

✅Multimodal Consistency:

Selected only those posts where text and image annotations matched

Resulted in a total of 8,219 consistent samples:

Train set: 6,574 posts

Test set: 1,644 posts

✅ Preprocessing Done Text:

Tokenized using BERT tokenizer (bert-base-uncased)

Extracted input_ids and attention_mask

Image:

Processed using ResNet-50

Extracted 2048-dimensional image features

The preprocessed data was saved in PyTorch .pt format:

train_human.pt and test_human.pt

Each contains: input_ids, attention_mask, image_vector, and label