Remove Mermaid diagrams; keep tables and text only

README.md

@@ -39,145 +39,32 @@ Fine-tuned [`microsoft/graphcodebert-base`](https://huggingface.co/microsoft/gra
 
 Part of [SolidityGuard](https://github.com/tanaymitra54/solidity_guard_razorpay) — used as a first-pass detector alongside Slither and an LLM auditor.
 
-## Architecture overview
+## How it fits in SolidityGuard
 
-How this model sits inside the SolidityGuard audit stack:
+1. **Input:** Solidity source  
+2. **Detectors:** Slither / patterns + **this Graph CodeBERT model**  
+3. **LLM agents:** Scanner → Analyzer → Exploit Gen / Fix Suggester  
+4. **Output:** Audit findings with severity and confidence  
 
-```mermaid
-flowchart TB
-    subgraph Input
-        SOL["Solidity source (.sol)"]
-    end
+## Model
 
-    subgraph Detectors["First-pass detectors"]
-        SL["Slither / patterns"]
-        GCB["Graph CodeBERT<br/>this model"]
-    end
+- Tokenizer → `max_length=512`  
+- Graph CodeBERT encoder (~125M params)  
+- Linear classification head → 12-class softmax → label + confidence  
 
-    subgraph LLM["LLM agents"]
-        SCAN["Scanner"]
-        AN["Analyzer"]
-        EX["Exploit Gen"]
-        FX["Fix Suggester"]
-    end
+## Training
 
-    subgraph Out["Output"]
-        REP["Audit findings<br/>+ severity + confidence"]
-    end
-
-    SOL --> SL
-    SOL --> GCB
-    SOL --> SCAN
-    SL --> SCAN
-    GCB --> SCAN
-    SCAN --> AN
-    AN --> EX
-    AN --> FX
-    SCAN --> REP
-    AN --> REP
-    EX --> REP
-    FX --> REP
-```
-
-## Model internals
-
-Sequence-classification head on Graph CodeBERT (RoBERTa-style encoder):
-
-```mermaid
-flowchart LR
-    A["Solidity source<br/>string"] --> B["Tokenizer<br/>max_length=512"]
-    B --> C["input_ids<br/>attention_mask"]
-    C --> D["Graph CodeBERT<br/>encoder<br/>~125M params"]
-    D --> E["[CLS] pooled<br/>representation"]
-    E --> F["Linear classifier<br/>12 logits"]
-    F --> G["Softmax"]
-    G --> H["label + confidence"]
-```
-
-## Training pipeline
-
-End-to-end fine-tune path used for this checkpoint:
-
-```mermaid
-flowchart TD
-    W["SmartBugs-Wild<br/>~47k contracts"] --> P["Parse + cap<br/>WILD_LIMIT=5000"]
-    R["SmartBugs-Results<br/>results_wild.json"] --> V["Tool consensus<br/>≥2 tools agree"]
-    P --> M["Labeled samples"]
-    V --> M
-    M --> S["Split 70 / 15 / 15<br/>train / val / test"]
-    S --> AUG["Light augmentation"]
-    AUG --> FT["Fine-tune<br/>Graph CodeBERT-base"]
-    FT --> ES["Early stopping<br/>on val macro-F1"]
-    ES --> BEST["checkpoints/best<br/>→ this Hub repo"]
-```
-
-## Data labeling logic
-
-```mermaid
-flowchart LR
-    C["Contract address"] --> T1["Tool A categories"]
-    C --> T2["Tool B categories"]
-    C --> T3["Tool N categories"]
-    T1 --> VOTE["Vote per category"]
-    T2 --> VOTE
-    T3 --> VOTE
-    VOTE --> Q{"votes ≥ 2?"}
-    Q -->|yes| LBL["Mapped vuln label<br/>e.g. reentrancy"]
-    Q -->|no| SAFE["safe"]
-```
-
-## Inference sequence
-
-```mermaid
-sequenceDiagram
-    participant U as Caller / API
-    participant D as GraphCodeBERTDetector
-    participant H as Hugging Face Hub
-    participant M as Model (GPU/CPU)
-
-    U->>D: scan(source_code)
-    alt first load
-        D->>H: from_pretrained(repo_id)
-        H-->>D: weights + config + tokenizer
-        D->>M: eval mode
-    end
-    D->>M: tokenize + forward
-    M-->>D: logits → softmax
-    alt label != safe AND conf ≥ threshold
-        D-->>U: Finding(issue_type, severity, confidence)
-    else
-        D-->>U: [] (no finding)
-    end
-```
-
-## Label taxonomy
-
-```mermaid
-mindmap
-  root((primary_vuln))
-    Safe
-      safe
-    Critical-leaning
-      reentrancy
-      access_control
-      tx_origin_auth
-      integer_overflow
-      unsafe_delegatecall
-    Medium / other
-      weak_randomness
-      unbounded_loop
-      other
-    Style / gas
-      redundant_storage
-      gas_optimization
-      best_practice
-```
+1. SmartBugs-Wild contracts (capped at 5,000)  
+2. Labels from SmartBugs-Results tool consensus (≥2 tools agree on a category; else `safe`)  
+3. Split 70 / 15 / 15 (train / val / test) with light augmentation  
+4. Fine-tune `microsoft/graphcodebert-base` with early stopping on validation macro-F1  
+5. Best checkpoint published here  
 
 ## Intended use
 
-- Input: Solidity source code (string)
-- Output: one of 12 labels (see below) + confidence
-- Best as a **screening** signal, not a sole security audit
+- Input: Solidity source code (string)  
+- Output: one of 12 labels + confidence  
+- Best as a **screening** signal, not a sole security audit  
 
 ## Labels
 
@@ -196,11 +83,6 @@ mindmap
 | 10 | `best_practice` | Low |
 | 11 | `other` | Medium |
 
-## Training data
-
-- **SmartBugs-Wild** contracts with tool-consensus labels from [SmartBugs results](https://github.com/smartbugs/smartbugs-results) (`metadata/results_wild.json`)
-- Cap: 5,000 contracts; train/val/test split 70/15/15 with light augmentation
-
 ## Held-out test metrics
 
 | Metric | Value |
@@ -213,15 +95,6 @@ mindmap
 | F1 `other` | 0.340 |
 | F1 `access_control` | 0.200 |
 
-```mermaid
-%%{init: {"theme": "neutral"}}%%
-xychart-beta
-    title "Per-class F1 on held-out test"
-    x-axis [safe, integer_overflow, reentrancy, other, access_control]
-    y-axis "F1" 0 --> 1
-    bar [0.694, 0.634, 0.461, 0.340, 0.200]
-```
-
 Labels are noisy (static-analysis consensus), so scores are moderate by design.
 
 ## Quick start
@@ -260,36 +133,26 @@ print(model.config.id2label[pred], float(probs[pred]))
 export GRAPHCODEBERT_PATH=tanaymitra01/graphcodebert-vulnerability-detector
 ```
 
-## Deploy / access map
+## Where to get the weights
 
-```mermaid
-flowchart TB
-    CKPT["Local checkpoint<br/>training/checkpoints/best"]
-    GH["GitHub LFS<br/>tanaymitra54/solidity_guard_razorpay"]
-    HF["Hugging Face Hub<br/>tanaymitra01/graphcodebert-vulnerability-detector"]
-
-    CKPT -->|git lfs push| GH
-    CKPT -->|hf upload| HF
-
-    HF --> COLAB["Colab / scripts"]
-    HF --> SPACE["HF Space / API host"]
-    HF --> LOCAL["Any machine<br/>from_pretrained"]
-    GH --> DEV["Dev clones"]
-```
+| Location | Path |
+|----------|------|
+| **Hugging Face (recommended)** | [`tanaymitra01/graphcodebert-vulnerability-detector`](https://huggingface.co/tanaymitra01/graphcodebert-vulnerability-detector) |
+| GitHub LFS | [`training/checkpoints/best/`](https://github.com/tanaymitra54/solidity_guard_razorpay/tree/main/training/checkpoints/best) in the SolidityGuard repo |
 
 ## Files
 
-- `model.safetensors` — weights
-- `config.json` — RobertaForSequenceClassification config + label maps
-- `label_map.json` — label list / id maps used in training
-- `README.md` — this model card
+- `model.safetensors` — weights  
+- `config.json` — RobertaForSequenceClassification config + label maps  
+- `label_map.json` — label list / id maps used in training  
+- `README.md` — this model card  
 
 ## Limitations
 
-- Tool-derived labels ≠ audited ground truth
-- Truncation at 512 tokens; large contracts lose context
-- Rare classes (e.g. access control) have low F1
-- Not a replacement for professional security review
+- Tool-derived labels ≠ audited ground truth  
+- Truncation at 512 tokens; large contracts lose context  
+- Rare classes (e.g. access control) have low F1  
+- Not a replacement for professional security review  
 
 ## Citation