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README.md

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+# tabular-decision-transformer-finance
+
+## Model Overview
+
+The `tabular-decision-transformer-finance` model is a specialized **Decision Transformer** adapted for classifying structured, tabular data (often seen in financial risk assessment). Instead of predicting actions in a sequence, this model interprets the feature vector (sequence of normalized features) and classifies the overall outcome (e.g., loan risk). It leverages the power of the attention mechanism to capture complex, non-linear interactions between features, outperforming traditional tree-based models on complex, high-dimensional datasets.
+
+## Model Architecture
+
+* **Base Architecture:** **Decision Transformer (`DecisionTransformerForSequenceClassification`)**. 
+* **Mechanism:** The model treats the ordered features of a tabular row (e.g., [Age, Income, Credit Score, ...]) as a sequence of tokens. The Transformer Encoder processes this feature sequence, applying self-attention to understand the relative importance and correlation of different features to the final outcome.
+* **Output:** The output of the final Transformer layer is passed through a simple linear classification head to predict one of three risk categories: **High Risk, Medium Risk, or Low Risk**.
+* **Input Features (Example Domain: Loan Risk):** Age, Annual Income, Credit Score, Loan Amount, Default History (Categorical).
+
+## Intended Use
+
+* **Financial Risk Assessment:** Classifying the risk level of a loan applicant, insurance claim, or investment.
+* **Structured Data Prediction:** Providing highly accurate predictions on datasets where feature interactions are complex (e.g., predicting customer churn, equipment failure).
+* **Feature Importance Analysis:** The internal attention weights can be used to provide interpretability on which features drove the classification decision.
+
+## Limitations and Ethical Considerations
+
+* **Interpretability:** While attention weights offer some insight, the overall black-box nature of the Transformer is harder to explain to regulators than a simple decision tree. This is a critical limitation in highly regulated sectors like finance.
+* **Feature Ordering:** Performance is sensitive to the order in which features are presented (encoded as the sequence). Features must be consistently ordered.
+* **Bias:** Inherits and amplifies biases present in the training data, potentially leading to unfair or discriminatory risk assessments based on proxies for sensitive attributes (e.g., income as a proxy for socioeconomic status). **Rigorous bias testing is required.**
+* **Input Normalization:** Requires all continuous features to be properly normalized or standardized before being passed as input.
+
+## Example Code
+
+To classify a new financial profile:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Conceptual loading (A tokenizer is often a custom feature processor here)
+model_name = "YourOrg/tabular-decision-transformer-finance"
+# Assuming a custom tokenizer/processor handles feature -> tensor conversion
+# tokenizer = TabularFeatureTokenizer.from_pretrained(model_name) 
+# model = DecisionTransformerForSequenceClassification.from_pretrained(model_name)
+
+# --- Conceptual Input Data ---
+# A new applicant's features, normalized and ordered:
+raw_features = [35, 75000, 720, 15000, 1] # Age, Income, Score, Amount, History (1=Yes)
+
+# Input preparation (conceptual: features are tokenized/embedded into a tensor)
+# In reality, this requires a specific processor to handle categorical/continuous embeddings.
+input_tensor = torch.tensor([raw_features], dtype=torch.float32) 
+
+# --- Conceptual Prediction ---
+# with torch.no_grad():
+#     outputs = model(input_tensor)
+#     logits = outputs.logits
+#     predicted_class_id = torch.argmax(logits, dim=1).item()
+
+# Predicted class mapping (based on config.json)
+predicted_class_id = 0 # Example result
+label_map = {0: "High Risk", 1: "Medium Risk", 2: "Low Risk"}
+prediction = label_map[predicted_class_id]
+
+print(f"Input Profile: Age 35, Score 720, Amount $15k")
+print(f"Predicted Loan Risk: **{prediction}**")