Push model using huggingface_hub.

README.md

@@ -1,162 +1,27 @@
----
-language:
-  - en
-license: mit
-library_name: phishbyte
-pipeline_tag: text-classification
-tags:
-  - phishing-detection
-  - email-security
-  - cybersecurity
-  - security
-  - pytorch
-  - from-scratch
-  - no-pretrained-weights
-  - cascading-inference
-  - lightweight
-  - explainable-ai
-  - nlp
-  - phishing
-  - spam-detection
-  - malware-detection
-  - threat-detection
-  - email-classification
-  - text-classification
-  - feature-engineering
-  - interpretable-ml
-datasets:
-  - ceas-2008
-metrics:
-  - f1
-  - precision
-  - recall
-  - accuracy
-model-index:
-  - name: phishbyte
-    results:
-      - task:
-          type: text-classification
-          name: Phishing Email Detection
-        dataset:
-          name: CEAS-2008
-          type: ceas-2008
-        metrics:
-          - type: f1
-            value: 0.948
-            name: F1 Score
-          - type: accuracy
-            value: 0.944
-            name: Accuracy
-          - type: precision
-            value: 0.9537
-            name: Precision
-          - type: recall
-            value: 0.9432
-            name: Recall
-widget:
-  - text: "From: PayPal Security <security@paypa1-alert.tk>\nReply-To: attacker@evil-domain.ru\nSubject: URGENT: Your account will be suspended\n\nDear Customer, your PayPal account has been suspended. Verify now at http://paypal-login.tk/verify"
-    example_title: "Phishing email example"
-  - text: "From: alice@company.com\nReply-To: alice@company.com\nSubject: Team lunch tomorrow\n\nHi everyone, lunch is at noon tomorrow in the usual spot. See you there!"
-    example_title: "Legitimate email example"
----
-
-# Phish_Byte
-
-A from-scratch PyTorch model for **email phishing detection**.
-**F1 0.948** on CEAS-2008. **12,545 parameters** (≈9,000× smaller than DistilBERT).
-**1,500+ emails/sec** on a laptop GPU. Every verdict explains itself.
-
-> **v3 in progress:** expanding to 50K parameters + 6-dataset corpus training.
-
-## Quick start
-
-```python
-from phishbyte import PhishByteEngine
-
-engine  = PhishByteEngine.from_pretrained("SamSec007/phishbyte")
-verdict = engine.analyze(raw_email_string)
-
-print(verdict.label)             # 'phishing'
-print(verdict.probability)       # 0.9735
-print(verdict.confidence)        # 'high'
-print(verdict.layer_used)        # 2
-print(verdict.feature_weights)   # per-feature attribution
-```
-
-## Why this exists
-
-Every phishing detection model on HuggingFace is a fine-tuned transformer —
-DistilBERT, BERT, RoBERTa. 65–110M parameters. ~250 MB on disk. ~50 ms/email.
-
-Phish_Byte is different:
-- Custom MLP trained **from scratch** — no pretrained weights
-- **29 engineered features** across domain, URL, SPF, subject, and character-level signals
-- **Cascading inference** — cheap rules handle obvious cases, MLP handles the rest
-- **Full email header analysis** including live SPF validation
-- Runs on **CPU without a GPU**
-- Every verdict includes **which signals fired and why**
-
-## Benchmarks (CEAS-2008, n=2,000 held-out)
-
-| Metric | Phish_Byte | DistilBERT fine-tuned |
-|--------|:----------:|:---------------------:|
-| F1 score | **0.948** | ~0.967 |
-| Parameters | **12,545** | 66,000,000 |
-| Model size | **52 KB** | ~250 MB |
-| Throughput (GPU) | **1,527/sec** | ~50/sec |
-| GPU required | **No** | Practically yes |
-| Header analysis | **Yes (SPF, DKIM)** | No |
-| Explainability | **29-feature attribution** | Token-level SHAP |
-
-## Feature signals (29 inputs)
-
-| Category | Features |
-|----------|----------|
-| Domain (5) | mismatch, Reply-To diff, Return-Path diff, freemail flag, brand impersonation |
-| URL (5) | HTTPS ratio, anchor mismatch, suspicious TLD, urgency, link density |
-| SPF (3) | fail, no record, no sending IP |
-| Subject (7) | urgency, security theme, brand name, currency, all-caps, fake RE, fake transaction ID |
-| Character-level (5) | caps ratio, digit ratio, special density, word length, HTML ratio |
-| Composite (4) | per-layer normalized scores |
-
-## Architecture
-
-```
-raw email
-  → Layer 1 (rule scorers, ~1ms) → confidence gate
-  → Layer 2 (custom MLP, ~3ms) → PhishVerdict
-    {label, probability, confidence, layer_used, feature_weights}
-```
-
-## Install
-
-```bash
-pip install huggingface_hub safetensors dnspython
-```
-
-```python
-from phishbyte import PhishByteEngine
-engine = PhishByteEngine.from_pretrained("SamSec007/phishbyte")
-verdict = engine.analyze(raw_email_string)
-```
-
-## Limitations
-
-- ~5% error rate (F1 0.948). Use as one signal in defence-in-depth.
-- Trained on CEAS-2008 (English, 2008-era phishing). Modern attack patterns may reduce recall.
-- SPF validation skipped during training on historical data — re-enables at inference time.
-
-## Citation
-
-```bibtex
-@software{phishbyte2026,
-  author = {Singh, Samratth},
-  title  = {Phish_Byte: Cascading from-scratch PyTorch phishing detection},
-  year   = {2026},
-  url    = {https://github.com/AnonymousSingh-007/Phish_Byte}
-}
-```
-
-## License
-
-MIT
+---
+library_name: phishbyte
+license: mit
+pipeline_tag: text-classification
+tags:
+- cascading-inference
+- cybersecurity
+- email-security
+- explainable-ai
+- from-scratch
+- lightweight
+- model_hub_mixin
+- nlp
+- no-pretrained-weights
+- phishing
+- phishing-detection
+- pytorch
+- pytorch_model_hub_mixin
+- spam-detection
+- text-classification
+- threat-detection
+---
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: https://github.com/AnonymousSingh-007/Phish_Byte
+- Paper: [More Information Needed]
+- Docs: [More Information Needed]

config.json

@@ -1,6 +1,10 @@
 {
   "dropout1": 0.3,
   "dropout2": 0.2,
-  "hidden_dim": 128,
-  "input_dim": 29
+  "dropout3": 0.1,
+  "hidden_1": 360,
+  "hidden_2": 180,
+  "hidden_3": 90,
+  "hidden_4": 48,
+  "input_dim": 85
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:81c474b9fe855039420d2a71cdb18305af57f4d49c5e6ae7d71ee3851ba17bf2
-size 52940
+oid sha256:155f6d652a88522b92a237deb4217aa5d6ba7e7ce6af92aa1d3cb9355e56af5b
+size 1032852