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README.md

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-library_name: transformers
-tags: []
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-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Glossary [optional]
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-## Model Card Contact
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+# MentalBERT V5 — Source-Aware Multi-Task Classifier
+
+**Architecture:** Dual-head MentalBERT (BertModel base + classification head + auxiliary source head)  
+**Dataset:** V5 (6 sources, 8 classes, ~88k samples)  
+**Test Accuracy:** 83.23%  |  **F1 Macro:** 0.8381
+
+## Load Pattern
+
+```python
+import torch
+import torch.nn as nn
+import joblib, json
+from transformers import BertModel, BertTokenizerFast
+from huggingface_hub import hf_hub_download
+
+# 1. Load BertModel base and tokenizer
+base = BertModel.from_pretrained('itsLu/mentalbert-v5-source-aware')
+tok  = BertTokenizerFast.from_pretrained('itsLu/mentalbert-v5-source-aware')
+
+# 2. Load config
+config_path = hf_hub_download('itsLu/mentalbert-v5-source-aware', 'inference_config.json')
+with open(config_path) as f:
+    cfg = json.load(f)
+
+# 3. Reconstruct classification head
+cls_head = nn.Linear(768, cfg['n_classes'])
+head_path = hf_hub_download('itsLu/mentalbert-v5-source-aware', 'cls_head.pt')
+cls_head.load_state_dict(torch.load(head_path, map_location='cpu'))
+
+# 4. Reconstruct wrapper model
+class InferenceModel(nn.Module):
+    def __init__(self, bert, head):
+        super().__init__()
+        self.bert    = bert
+        self.dropout = nn.Dropout(0.1)
+        self.head    = head
+    def forward(self, input_ids, attention_mask):
+        out    = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        pooled = out.pooler_output
+        return self.head(self.dropout(pooled))
+
+model = InferenceModel(base, cls_head).eval()
+
+# 5. Inference
+le_path = hf_hub_download('itsLu/mentalbert-v5-source-aware', 'label_encoder.joblib')
+le = joblib.load(le_path)
+
+def predict(text):
+    enc   = tok(text, max_length=128, padding='max_length',
+                truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        logits = model(enc['input_ids'], enc['attention_mask'])
+    probs = torch.softmax(logits, dim=1).squeeze().numpy()
+    idx   = probs.argmax()
+    return le.classes_[idx], float(probs[idx])
+
+label, prob = predict("I can't stop thinking about how worthless I am.")
+print(label, f'{prob:.2%}')
+```
+
+## Classes
+- Anxiety
+- Bipolar
+- Depression
+- Directed Aggression
+- Normal
+- Personality Disorder
+- Stress
+- Suicidal
+
+## Source Reliability Weights
+| Source | Reliability |
+|--------|-------------|
+| cssrs | 1.0 |
+| olid | 1.0 |
+| kaggle_bpd | 0.95 |
+| huggingface | 0.7 |
+| kaggle | 0.7 |
+| swmh | 0.5 |