from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
import torch

MODEL = "Sonje03/phishlens-distilbert"

tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.eval()

email = "Your account has been suspended. Click here to verify..."
inputs = tok(email, truncation=True, max_length=256, return_tensors="pt")
with torch.no_grad():
    probs = F.softmax(model(**inputs).logits, dim=-1)[0]
print(f"P(Safe)={probs[0]:.3f}   P(Phishing)={probs[1]:.3f}")

Training details

Base model: distilbert-base-uncased (66M params)
Epochs: 3
Optimizer: AdamW, learning rate 2e-5
Batch size: 16, mixed precision (FP16)
Max sequence length: 256 tokens
Hardware: single NVIDIA T4 GPU (Google Colab)

Training corpora

Source	Role	Count
zefang-liu/phishing-email-dataset (MeAJOR Corpus)	Baseline text training	18,650
AreLit/PhishNChips	Modern workplace legits (augmentation)	1,333
cybersectony/PhishingEmailDetectionv2.0	Augmentation legits	11,322
`synthetic_legit_emails.csv`	NG-domain hand-templated	150

The augmented corpus dramatically lowers false positives on Nigerian-domain emails (banks, hospitals, telcos, university) that share template wording with phishing.

Intended use

Designed to be deployed behind the PhishLens FastAPI backend as one of three voting agents (text · URL · headers), with a trusted-domain allowlist that further reduces false positives on institutional senders.

Not intended for production phishing defence on its own — pair it with URL reputation services and DKIM/SPF/DMARC verification for any real deployment.

Limitations

English-only. No support for French / Hausa / Yoruba phishing.
PDF attachments are ignored. Only text/plain and text/html parts of an .eml are read.
Niche marketing / recruitment false positives can still occur.

License

MIT.

Citation

bibtex

@misc{phishlens2026,
  title  = {Smart Phishing Detection System Using Natural Language Processing Techniques},
  year   = {2026},
  school = {Nile University of Nigeria, Department of Cybersecurity},
  note   = {B.Sc. Final Year Project}
}