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language: |
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- en |
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--- |
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Source: CrisisMMD (Alam et al., 2017) |
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Data Type: Multimodal — each sample includes: |
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tweet_text (social media text) |
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tweet_image (corresponding image from the tweet) |
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Total Samples Used: ~18,802(from the dataset) |
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Class Labels: |
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0 → Non-informative |
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1 → Informative |
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Collect only values where tweet_text and tweet_image are equal. (thus collected 12,743 tweets and convert it into test and train .pt files) |
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✅ Preprocessing Done Text: |
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Tokenized using BERT tokenizer (bert-base-uncased) |
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Extracted input_ids and attention_mask |
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Image: |
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Processed using ResNet-50 |
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Extracted 2048-dimensional feature vectors |
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Label: |
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Encoded to 0 or 1 as per class |
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The final preprocessed dataset was saved as .pt files: |
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train_info.pt |
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test_info.pt |
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Each contains: input_ids, attention_mask, image_vector, and label tensors. |
