Update README.md

README.md

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-That's a great step! A professional Model Card is essential for Hugging Face.
+# Task Classification Model (ICM)
 
-I will generate a formal, well-structured `README.md` file that clearly documents your ICM module, including its architecture, training setup, and how others can use it.
+## Model Description
 
+A BERT-based sequence classification model that routes computer vision questions to appropriate specialized modules. Classifies questions into 4 task categories: VQA, Captioning, Grounding, and Geometry.
 
-http://googleusercontent.com/immersive_entry_chip/0
+- **Repository:** beingamanforever/ICM
+- **Base Model:** bert-base-uncased
+- **Task:** 4-way Sequence Classification
+
+## Labels
+
+| ID | Label | Description |
+|---|---|---|
+| 0 | vqa | Visual Question Answering ("What color is the car?") |
+| 1 | captioning | Image Description ("Describe the sunset.") |
+| 2 | grounding | Object Localization ("Find the person in the image.") |
+| 3 | geometry | Spatial/Metric Queries ("Calculate the area of the red box.") |
+
+## Architecture
+
+BERT-Base encoder + 3-layer MLP classifier on [CLS] token:
+
+- Layer 1: Linear(768 → 256) + ReLU + Dropout(0.1)
+- Layer 2: Linear(256 → 128) + ReLU + Dropout(0.1)  
+- Layer 3: Linear(128 → 4)
+
+## Training
+
+| Hyperparameter | Value |
+|---|---|
+| Samples | 1,600 (400 per class) |
+| Epochs | 5 |
+| Learning Rate | 2e-5 |
+| Batch Size | 32 |
+| Optimizer | AdamW |
+| Loss | Cross Entropy |
+
+**Data:** Synthetic questions from balanced JSON files (vqa_qs.json, captioning_qs.json, grounding_qs.json, geometry_qs.json)
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "beingamanforever/ICM"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+questions = [
+    "What is the distance between the two trees?",
+    "Describe what the child is wearing.",
+    "Is the traffic light green?",
+    "Box the location of the blue umbrella."
+]
+
+inputs = tokenizer(questions, return_tensors="pt", padding=True, truncation=True)
+with torch.no_grad():
+    logits = model(**inputs).logits
+    predictions = torch.argmax(logits, dim=-1)
+
+for q, pred in zip(questions, predictions):
+    print(f"{q} → {model.config.id2label[pred.item()]}")
+```
+
+## Limitations
+
+- **Synthetic Training Data:** May not generalize to complex real-world queries
+- **Text-Only:** Processes questions without image context
+- **Domain Scope:** Optimized for vision task routing, not general NLP classification
+
+## Intended Use
+
+- Automatic query routing in multimodal AI pipelines
+- VQA dataset analysis and taxonomy studies
+- Educational demonstrations of vision task classification