Update README.md

README.md

@@ -38,8 +38,10 @@ model-index:
 ---
 
 # EmCoder
-> **Probabilistic Emotion Recognition & Uncertainty Quantification**<br>**28 Emotion multi-label classifier trained with MC Dropout methodology**
-
+<blockquote>
+  <b>Probabilistic Emotion Recognition & Uncertainty Quantification</b><br>
+  <b>28 Emotion multi-label classifier trained with MC Dropout methodology</b>
+</blockquote>
 
 
 Unlike standard classifiers, EmCoder quantifies what it doesn't know using Monte Carlo Dropout, making it suitable for high-stakes AI pipelines.<br>
@@ -96,94 +98,7 @@ uncertainty = probs_all.std(dim=0) # Epistemic Uncertainty (Standard Deviation)
 
 
 ## Model Architecture
-```mermaid
-flowchart LR
-
-subgraph InputGroup["Input Operations"]
-    direction TB
-    MCD_Loop(["MC-Inference Loop: N_samples"]):::LoopNode
-    ids["Batch IDs"]
-    mask["Batch Mask"]
-end
-
-subgraph EmCoderCore["EmCoder Core"]
-    direction LR
-    tok_emb["Token Embedding"]
-    ln_in["Input LayerNorm"]
-    Transformer["Transformer Encoder"]
-    final_norm["Final LayerNorm"]
-    Dropout1[("MC-Dropout")]
-    Dropout2[("MC-Dropout")]
-end
-
-subgraph Row1[" "]
-    direction LR
-    InputGroup
-    EmCoderCore
-end
-
-subgraph MLP["Classifier MLP"]
-    L_lin["Linear 1"]
-    Dropout3[("MC-Dropout")]
-    GELU["GELU"]
-    F_lin["Final Linear"]
-end
-
-subgraph ClassifierHead[" "]
-    direction TB
-    pool["Masked Mean Pooling"]
-    MLP
-end
-
-subgraph Row2[" "]
-    direction LR
-    ClassifierHead
-    Out(["Class LogitsMC
-    (n_samples, B, 28)"])
-
-    Avg["Bayesian Post-processing"]
-end
-
-tok_emb ==> ln_in
-ln_in -.-> Dropout1
-Dropout1 ==> Transformer
-Transformer -.-> Dropout2
-Dropout2 ==> final_norm
-MCD_Loop -.-> ids
-ids ==> tok_emb
-final_norm ==> pool
-mask ==> pool
-pool ==> L_lin
-L_lin -.-> Dropout3
-Dropout3 ==> GELU
-GELU ==> F_lin
-F_lin ==> Out
-Out ==> Avg
-mask ==> Transformer
-
-classDef MCD fill:#424242,stroke:#fbc02d,stroke-width:2px,stroke-dasharray: 5 5,color:#fff
-classDef OutNode fill:#0d47a1,stroke:#1976d2,stroke-width:3px,color:#fff,font-weight:bold
-classDef BayesNode fill:#3e2723,stroke:#8d6e63,stroke-width:2px,stroke-dasharray: 3 3,color:#fff
-classDef LoopNode fill:#263238,stroke:#78909c,stroke-width:2px,color:#fff,font-style:italic
-classDef LightNode fill:#212121,stroke:#90a4ae,color:#fff
-
-class MCD_Loop LoopNode
-class ids,mask,tok_emb,ln_in,Transformer,final_norm,L_lin,GELU,F_lin,pool LightNode
-class Dropout1,Dropout2,Dropout3 MCD
-class Out OutNode
-class Avg BayesNode
-
-style InputGroup fill:#1a1a1a,stroke:#444,color:#fff
-style EmCoderCore fill:#2d1a2d,stroke:#6a1b9a,color:#fff
-style MLP fill:#212121,stroke:#455a64,color:#fff
-style ClassifierHead fill:#012a4a,stroke:#01497c,color:#fff
-style Row1 fill:none,stroke:none
-style Row2 fill:none,stroke:none
-
-linkStyle 2 stroke:#fbc02d,stroke-width:2px,fill:none
-linkStyle 5 stroke:#fbc02d,stroke-width:2px,fill:none
-linkStyle 11 stroke:#fbc02d,stroke-width:2px,fill:none
-```
+![EmCoder Architecture](outputs/architecture.svg)
 
 
 ### Optimization
@@ -250,44 +165,7 @@ $$
 
 
 ## Workflow
-```mermaid
-flowchart LR
-    classDef StageNode fill:#121212,stroke:#546e7a,color:#fff;
-    classDef HighlightNode fill:#4e342e,stroke:#ff7043,stroke-width:2px,color:#fff,font-weight:bold;
-
-    subgraph PT ["Phase 1: Pre-training"]
-        direction TB
-        OWT[(OpenWebText)]:::StageNode --> MLM[Masked Language Modeling]:::StageNode
-        MLM --> Core[Save EmCoderCore]:::StageNode
-    end
-
-    subgraph FT ["Phase 2: Fine-tuning"]
-        direction TB
-        Core --> Init[Init ClassificationHead]:::StageNode
-        GE[(GoEmotions)]:::StageNode --> WBT[Bayesian Fine-tuning]:::HighlightNode
-        WBT --> LogW[Log-weighted BCE Loss]:::StageNode
-        LogW --> Freeze[Step 0-500: Encoder Frozen]:::StageNode
-    end
-
-    subgraph EV ["Phase 3: Testing & Inference"]
-        direction TB
-        Freeze --> MCD[MC Dropout Inference]:::HighlightNode
-        MCD --> Unc[Uncertainty Estimation]:::HighlightNode
-        
-        subgraph Metrics ["Analysis"]
-            Unc --> EPI[Epistemic: Model Confidence]:::StageNode
-            Unc --> ALE[Aleatoric: Data Ambiguity]:::StageNode
-            Unc --> CM[Test set metrics]:::StageNode
-        end
-    end
-
-    style PT fill:#0d1b2a,stroke:#1b263b,color:#fff
-    style FT fill:#2e1500,stroke:#5d2a00,color:#fff
-    style EV fill:#1b2e1b,stroke:#2d4a2d,color:#fff
-    style Metrics fill:#000,stroke:#333,color:#fff
-
-    linkStyle default stroke:#aaa,stroke-width:2px;
-```
+![EmCoder Workflow](outputs/workflow.svg)
 
 
 ### Note