my-awesome-model

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my-awesome-model

By using supervised fine-tuning on the ucirvine/sms_spam dataset, you transitioned the model from general language understanding to a specialized security tool capable of recognizing adversarial text patterns and phishing intent.

• Loss: 0.0414
• Accuracy: 0.994
• F1: 0.9806

Model description

The model is a fine-tuned DistilBERT architecture specifically optimized for classifying SMS and email strings as either Ham (safe) or Spam (malicious). By utilizing contextual embeddings and supervised fine-tuning on the ucirvine/sms_spam dataset, it identifies intent and adversarial text patterns that simpler keyword filters often miss.

Intended uses & limitations

The model is designed for cybersecurity applications, serving as a first layer of defense to identify phishing attempts and audit outbound customer support messages for spam-like markers. However, it is limited by a temporal bias toward older SMS spam patterns and requires GPU acceleration to maintain the real-time performance necessary for its auditor applications.

How to run the code

import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification

1. Define the model ID from Hugging Face

model_id = "kinggreco/my-awesome-model"

2. Load the tokenizer and model

tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSequenceClassification.from_pretrained(model_id)

3. Prepare your input text

text = "URGENT: Your account has been flagged. Click here to verify now!"

4. Tokenize and predict

inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad(): logits = model(**inputs).logits prediction = torch.argmax(logits, dim=-1).item()

5. Output the result (0 = Ham, 1 = Spam)

print(f"Prediction: {'SPAM' if prediction == 1 else 'HAM'}")

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 5.8.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2