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shallowblueQAQ/
PsySym-model

Name: PsySym-model
Availability: InStock

safetensors

mental-health

social-media

symptom-identification

disease-detection

cc-by-nc-4.0

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🧩 PsySym: Symptom Identification & Disease Detection System

📖 Model Overview

The relevant training code is available here:

GitHub

What is PsySym?

PsySym is a comprehensive framework for interpretable mental disease detection on social media. Unlike "black-box" models that directly predict diseases from text, PsySym first identifies specific psychiatric symptoms defined in clinical manuals (DSM-5) and then uses these symptom profiles to detect mental disorders.

Figure 1: Comparison between pure-text and symptom-assisted mental disease detection.

This repository contains the models described in the paper "Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media" (EMNLP 2022).

The system consists of three distinct components:

Symptom Relevance Model (relevance_model): A multi-label classifier that identifies 38 symptom categories from social media sentences.
Symptom Status Model (status_model): A model that determines the uncertainty status of the identified symptoms (e.g., distinguishing "I have insomnia" from "I don't have insomnia").
Disease Detection Model (disease_model): A CNN-based model that predicts mental disorders (e.g., Depression, Anxiety) based on the symptom feature sequences extracted from user timelines.

Architecture

Relevance & Status Models: Based on BERT (MentalBERT-base) with a linear classification head.
Disease Model: A custom CNN that aggregates symptom features across a user's posting history.

📂 Repository Structure

This repository uses subfolders to store the weights for different models.

Subfolder	Task Description	Input	Output
`relevance_model/`	Identifies which of the 38 symptoms are present.	Text (Sentence)	Logits (Dim: 38)
`status_model/`	Estimates the uncertainty of the symptom.	Text (Sentence)	Logits (Dim: 1)
`disease_model/{disease_name}/`	Detects a specific mental disease (e.g., `depression`, `anxiety`).	Symptom Features Vector	Logits (Dim: 1)

Figure 2: The proposed symptom-assisted MDD pipeline.

🚀 Quick Start (Copy & Run)

Since these models use custom architectures, you must define the model classes locally before loading the weights.

1. Installation

pip install transformers torch huggingface_hub

2. Define Model Architectures

A. For Relevance & Status Models (BERT-based)

python

import torch
from torch import nn
from transformers import AutoModel, AutoConfig

class BERTDiseaseClassifier(nn.Module):
    def __init__(self, model_type, num_symps) -> None:
        super().__init__()
        self.model_type = model_type
        self.num_symps = num_symps
        self.encoder = AutoModel.from_pretrained(model_type)
        self.dropout = nn.Dropout(self.encoder.config.hidden_dropout_prob)
        self.clf = nn.Linear(self.encoder.config.hidden_size, num_symps)

    def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, **kwargs):
        outputs = self.encoder(input_ids, attention_mask, token_type_ids)
        x = outputs.last_hidden_state[:, 0, :]   # [CLS] pooling
        x = self.dropout(x)
        logits = self.clf(x)
        return logits

B. For Disease Detection Models (CNN-based)

python

import torch
from torch import nn
from torch.nn import functional as F
from transformers import PreTrainedModel, PretrainedConfig

class DiseaseConfig(PretrainedConfig):
    model_type = "kmax_mean_cnn"
    def __init__(self, in_dim=38, filter_num=50, filter_sizes=(2, 3, 4, 5, 6), dropout=0.2, max_pooling_k=5, **kwargs):
        super().__init__(**kwargs)
        self.in_dim = in_dim
        self.filter_num = filter_num
        self.filter_sizes = filter_sizes
        self.dropout = dropout
        self.max_pooling_k = max_pooling_k

def kmax_pooling(x, k):
    return x.sort(dim = 2)[0][:, :, -k:]

class KMaxMeanCNN(PreTrainedModel):
    config_class = DiseaseConfig
    def __init__(self, config):
        super().__init__(config)
        self.filter_num = config.filter_num
        self.filter_sizes = config.filter_sizes
        self.hidden_size = len(config.filter_sizes) * config.filter_num
        self.max_pooling_k = config.max_pooling_k
        self.convs = nn.ModuleList([nn.Conv1d(config.in_dim, config.filter_num, size) for size in config.filter_sizes])
        self.dropout = nn.Dropout(config.dropout)
        self.fc = nn.Linear(self.hidden_size, 1)
        self.post_init()

    def forward(self, input_seqs, **kwargs):
        # input_seqs shape: [Batch, SeqLen, InDim]
        input_seqs = input_seqs.transpose(1, 2) 
        x = [F.relu(conv(input_seqs)) for conv in self.convs]
        x = [kmax_pooling(item, self.max_pooling_k).mean(2) for item in x]
        x = torch.cat(x, 1)
        x = self.dropout(x)
        logits = self.fc(x)
        return logits

3. Usage Example

A. Loading Relevance & Status Models

Unlike standard BERT models, mental/mental-bert-base-uncased is a gated (non-public) model on Hugging Face. Users must log in to their Hugging Face account and obtain access permission before downloading it.

For convenience and reproducibility, we recommend downloading MentalBERT locally and replacing the MentalBERT path in the code with the local checkpoint path.

🔐 How to Obtain a Hugging Face Access Token

To download and use gated models (e.g., mental/mental-bert-base-uncased), you need a Hugging Face account and a valid access token.

Please follow the steps below:

Step 1: Create a Hugging Face Account

If you do not already have one, create an account at:

https://huggingface.co/join

Step 2: Generate an Access Token

Log in to your Hugging Face account.
Go to Settings → Access Tokens.
Click “Create new token”.
Choose Read permission (this is sufficient for downloading models).
Give the token a name (e.g., mental-bert-access).
Click Create token and copy the token.

⚠️ Keep your token private. Do not share it or commit it to public repositories.

Step 3: Log In Programmatically

Before loading the model, log in using the Hugging Face Hub API:

python

from huggingface_hub import login

login()  # Paste your access token when prompted

This step is required when running code in online environments such as Google Colab or remote servers.

Step 4: Request Access to MentalBERT

The model mental/mental-bert-base-uncased is a gated repository. You must explicitly request access on its Hugging Face model page:

https://huggingface.co/mental/mental-bert-base-uncased

Once access is granted, you will be able to download the model using your access token.

python

import torch
from transformers import AutoConfig, AutoTokenizer
from huggingface_hub import hf_hub_download, login
# login()  # Required when running in an online environment (e.g., Google Colab)
# from model import BERTDiseaseClassifier

repo_id = "shallowblueQAQ/PsySym-model"
subfolder = "relevance_model"
# subfolder = "status_model"

# 1. Load Config & Tokenizer
config = AutoConfig.from_pretrained(repo_id, subfolder=subfolder)
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)

# 2. Initialize Model Architecture
# model = BERTDiseaseClassifier(model_type="mental/mental-bert-base-uncased", num_symps=len(config.id2label))
# Replace `/path/to/mental-bert-base-uncased` with the actual local path where MentalBERT is stored.
model = BERTDiseaseClassifier(model_type="/path/to/mental-bert-base-uncased", num_symps=len(config.id2label))

# 3. Load Weights
weights_path = hf_hub_download(repo_id=repo_id, subfolder=subfolder, filename="pytorch_model.bin")
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

# 4. Inference
text = "I had a headache yesterday." if subfolder == "relevance_model" else "Does taking away distractions from some one that has ADD distract the person more or less?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    logits = model(**inputs)
    probs = torch.sigmoid(logits)

# Display Predictions (Multi-label)
threshold = 0.5
for i, prob in enumerate(probs[0]):
    if prob > threshold:
        print(f"Detected: {config.id2label[i]} ({prob:.4f})")

B. Loading Disease Detection Models

Note: The disease model takes symptom feature vectors as input (Shape: [Batch, Seq_Len, 38]), not raw text.

python

import torch
from transformers import AutoConfig
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

# 1. Define the Model Architecture (Must match model_hf_disease.py)
# (Copy the KMaxMeanCNN class definition from the "Define Model Architectures" section above)
# model = KMaxMeanCNN(config) ... 

# 2. Configuration
repo_id = "shallowblueQAQ/PsySym-model"
disease_name = "depression"  # Options: depression, anxiety, autism, adhd, schizophrenia, bipolar, ocd, ptsd, eating.
subfolder = f"disease_model/{disease_name}"

# 3. Load Config
config = DiseaseConfig.from_pretrained(repo_id, subfolder=subfolder)

# 4. Initialize Model
model = KMaxMeanCNN(config)

# 5. Load Weights
weights_path = hf_hub_download(repo_id=repo_id, subfolder=subfolder, filename="model.safetensors")
state_dict = load_file(weights_path)
model.load_state_dict(state_dict)

model.eval()

# 6. Inference Example
# Input: A sequence of symptom probabilities (from Relevance Model)
# Shape: [Batch_Size, Sequence_Length, Feature_Dim(38)]
# Example: Batch=1, User has 50 posts, each post has 38 symptom features
dummy_input = torch.randn(1, 50, 38) 

with torch.no_grad():
    # The model expects 'input_seqs'
    outputs = model(input_seqs=dummy_input)
    logits = outputs  # Shape: [1, 1]
    
    # Convert logits to probability
    prob = torch.sigmoid(logits).item()

print(f"Disease Prediction ({disease_name}): {prob:.4f}")
# Output > 0.5 implies the disease is detected

⚠️ Ethical Considerations & Limitations

Research Use Only: This model is intended for research purposes only. It is not a diagnostic tool and must not be used for self-diagnosis or clinical decision-making.
Bias & Errors: The model is trained on Reddit data and may reflect specific linguistic styles or biases present in that community. It may not generalize perfectly to other platforms or populations.
Data Privacy: The training data involves sensitive mental health disclosures. While the model weights do not directly expose user data, outputs should be handled with care to protect user privacy.

Data Availability

This model was trained on PsySym, a subset derived from the SMHD (Self-reported Mental Health Diagnoses) dataset.

Due to the strict Data Usage Agreement of SMHD, we cannot publish the original dataset. Researchers interested in the data must apply for access directly from the creators of SMHD (Cohan et al., 2018).

Citation

If you use this model, please cite our paper:

bibtex

@inproceedings{zhang2022symptom,
  title={Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media},
  author={Zhang, Zhiling and Chen, Siyuan and Wu, Mengyue and Zhu, Kenny},
  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  pages={9970--9985},
  year={2022}
}