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README.md

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 ---
+language:
+- en
 license: mit
+library_name: transformers
+tags:
+- cybersecurity
+- phishing-detection
+- nlp
+- bert
+- social-engineering
+datasets:
+- phishing-dataset
+- email-phishing-detection
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+- roc_auc
+pipeline_tag: text-classification
+widget:
+- text: "URGENT: Your bank account will be suspended in 24 hours. Click here to verify your identity."
+  example_title: "High Urgency Phishing Lure"
+- text: "Hi team, please find attached the minutes of our quarterly project review meeting."
+  example_title: "Benign Communication"
 ---
+
+# Ph5 Text Classifier: Social Engineering & Email Phishing Detector (BERT-base)
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Model-BERT--base-blue.svg" alt="Model">
+  <img src="https://img.shields.io/badge/Accuracy-97.22%25-brightgreen.svg" alt="Accuracy">
+  <img src="https://img.shields.io/badge/F1--Score-96.67%25-success.svg" alt="F1 Score">
+  <img src="https://img.shields.io/badge/ROC--AUC-0.9961-red.svg" alt="ROC-AUC">
+  <img src="https://img.shields.io/badge/Latency-34.2ms-orange.svg" alt="Latency">
+  <img src="https://img.shields.io/badge/License-MIT-purple.svg" alt="License">
+</p>
+
+## Model Summary
+
+The **Ph5 Text Classifier** is a fine-tuned `bert-base-uncased` language model engineered to detect psychological manipulation, urgency cues, coercive authority claims, and credential-harvesting lures across emails, SMS messages, and HTML message payloads.
+
+It functions as the core semantic inspection branch in the **Ph5 Multimodal Cybersecurity Framework**, and is directly coupled with an optical character recognition (OCR) fallback engine to analyze text embedded within attached images and QR-code lures.
+
+---
+
+## Performance & Evaluation Benchmark
+
+Evaluated on an independent, strictly isolated held-out test split of **11,652 deduplicated messages** (6,747 Benign [57.90%], 4,905 Phishing [42.10%]):
+
+| Metric | Measured Score | Standard Deviation (5-Fold CV) |
+| :--- | :--- | :--- |
+| **Accuracy** | **97.22%** | ± 0.10% |
+| **Precision** | **97.51%** | ± 0.08% |
+| **Recall** | **95.84%** | ± 0.12% |
+| **F1-Score** | **96.67%** | ± 0.09% |
+| **ROC-AUC** | **0.9961** | ± 0.0004 |
+| **False Positive Rate** | **1.78%** (FP = 120 / 6747) | — |
+| **False Negative Rate** | **4.16%** (FN = 204 / 4905) | — |
+| **Mean Inference Time** | **34.2 ms** | Batch Size = 1 (RTX 3050 Laptop GPU) |
+
+### Baseline Comparison
+
+| Architecture | Accuracy | Precision | Recall | F1-Score |
+| :--- | :--- | :--- | :--- | :--- |
+| TF-IDF + Multinomial Naive Bayes | 86.85% | 88.32% | 79.25% | 83.54% |
+| TF-IDF + Logistic Regression | 88.27% | 89.24% | 82.02% | 85.48% |
+| **Ph5 BERT-base (This Model)** | **97.22%** | **97.51%** | **95.84%** | **96.67%** |
+
+---
+
+## Confusion Matrix (Test Split: N = 11,652)
+
+```text
+               Predicted
+           Benign   Phishing
+Benign      6627      120
+Phishing     204     4701
+```
+
+---
+
+## Intended Use & Integration
+
+### Python (Transformers & PyTorch)
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model and tokenizer from Hugging Face Hub
+repo_id = "xynaxdev/ph5-text-bert"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForSequenceClassification.from_pretrained(repo_id)
+model.eval()
+
+# Sample message text
+message = "Urgent: Your account credentials have expired. Verify immediately to avoid permanent lockout."
+
+# Tokenize and run forward pass
+inputs = tokenizer(message, return_tensors="pt", max_length=256, truncation=True)
+with torch.no_grad():
+    logits = model(**inputs).logits
+    probabilities = torch.softmax(logits, dim=-1).squeeze().tolist()
+
+p_benign, p_phish = probabilities[0], probabilities[1]
+print(f"Benign Probability:   {p_benign:.4f}")
+print(f"Phishing Probability: {p_phish:.4f}")
+# Output: Phishing Probability: 0.9872
+```
+
+---
+
+## Training Configuration & Data Provenance
+
+- **Pretrained Backbone:** `bert-base-uncased` (12 layers, 768 hidden dimensions, 12 attention heads, 110M parameters)
+- **Classification Head:** Linear projection (768 -> 2)
+- **Training Samples:** 77,677 messages total (54,374 train / 11,651 val / 11,652 test)
+  - Sourced from curated open-access corpora on Hugging Face: `ealvaradob/phishing-dataset` and `mrm8488/email-phishing-detection`
+- **Optimizer:** AdamW (beta1=0.9, beta2=0.999, weight decay = 0.01)
+- **Learning Rate:** 2e-5 with linear learning rate warmup
+- **Batch Size:** 16 (Trained with PyTorch Automatic Mixed Precision)
+- **Epochs:** 3 epochs (Early stopping checkpoint)
+
+---
+
+## Citation & Framework
+
+```bibtex
+@article{devi2026ph5,
+  title={Ph5: A Multimodal Framework for Cross-Modality Evasion-Resilient Phishing Detection in Multi-Vector Environments},
+  author={Devi, Ritu and Singh, Deepti and Sah, Akash},
+  journal={Journal of Computer & Mathematical Sciences / JCMM},
+  year={2026}
+}
+```

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eb9dabad5fecb9afcd9a8cc53f5c754f5a69089d47fef394ebbf008535f64045
+size 437958648

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}