Re-upload model using PyTorch .bin format

README.md

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+🧠 Joint fMRI-Text Model
+This model jointly predicts cognitive response type, trial type, and generates a 3D fMRI-like brain activation tensor based on natural language input and user-level metadata.
+
+🧩 Inputs
+Text: a belief or statement in natural language (e.g., "Handwashing reduces disease risk."), processed using distilbert-base-uncased.
+
+Metadata: a vector of 14 user features including:
+
+Scaled continuous inputs: Age_scaled, Openness_scaled, Conscientiousness_scaled, Extraversion_scaled, Agreeableness_scaled, Neuroticism_scaled, ICAR_Total_scaled, MOCA_scaled, VMN_Sum_scaled
+
+Encoded categorical features: Gender_encoded, Education_encoded, Ethnicity_fused_encoded, Income_level_encoded, VCBS_cat_encoded
+
+🎯 Outputs
+Response Type: probabilities over 4 possible response categories
+
+Trial Type: probabilities over 3 trial categories
+
+fMRI Tensor: synthetic output of shape (74 × 74 × 52) representing brain activity across four timepoints
+
+🚀 Example Usage
+from transformers import AutoTokenizer
+from joint_fmri_model import JointFMRIModelWithHub
+import torch
+
+model = JointFMRIModelWithHub.from_pretrained("kenchenxingyu/joint-fmri-text-model")
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+text = "I think regular hand washing reduces disease risk."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    text_vec = model.text_encoder(inputs["input_ids"]).squeeze(0)
+
+meta_input = torch.rand(1, 14)  # Replace with realistic metadata
+
+output = model(text_vec.unsqueeze(0), meta_input)
+
+print("Response:", output["response_probs"])
+print("Trial:", output["trial_probs"])
+print("fMRI shape:", output["fmri"].shape)
+📂 Files
+pytorch_model.bin: trained model weights
+
+config.json: model configuration
+
+README.md: this file
+
+🏷 License
+This model is released under an open academic research license. For other use cases, please contact the author.

config.json

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+{"node_dim": 1, "text_dim": 768, "meta_dim": 14, "hidden_dim": 128, "num_nodes": 47, "num_response_classes": 4, "num_trial_classes": 3}

pytorch_model.bin

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