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README.md

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+Source: CrisisMMD (Alam et al., 2017)
+
+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.
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+✅ Model Architecture
+A custom multimodal neural network combining both BERT and ResNet features:
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+Component	Details
+Text Encoder	BERT base model (bert-base-uncased) – outputs pooler_output (768-d)
+Image Encoder	ResNet-50 pre-extracted features (2048-d)
+Fusion	Concatenation → FC layers → Softmax
+Classifier	Fully connected layers with BatchNorm, ReLU, Dropout
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+✅ Training Setup
+Loss Function: CrossEntropyLoss
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+Optimizer: AdamW
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+Scheduler: StepLR (γ = 0.9)
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+Epochs: 8
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+Batch Size: 16
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+Device: CUDA (if available)
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+✅ Evaluation Metrics
+Accuracy
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+Precision
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+Recall
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+F1 Score
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+✅ Test Accuracy : 0.8518
+✅ Precision     : 0.8289
+✅ Recall        : 0.8032
+✅ F1 Score      : 0.8142