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URLGuardian/README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- distilbert/distilbert-base-multilingual-cased
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- code
+- cyber
+---
+
+
+# Transformer
+
+This is a transformers model fine tuned for malicious URL detection. Given a FQDN URL it outputs probability of it to be malicious identifying common suspicious pattern.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Anvilogic
+- **Model Type:** Transformer
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Finetuned from model:** [distilbert](https://huggingface.co/distilbert/distilbert-base-cased)
+- **Language(s) (NLP):** Multilingual
+- **License:** MIT
+
+### Full Model Architecture
+
+```
+DistilBERT:
+  name: "distilbert-base-cased"
+  params:
+    layers: 6
+    hidden_size: 768
+    attention_heads: 12
+    ff_dim: 3072
+    max_seq_len: 512
+    vocab_size: 28996
+    total_params: 66M
+    activation: "gelu"
+```
+
+## Usage
+
+### Direct Usage
+
+First install the Transformers library:
+
+```bash
+pip install -U transformers
+```
+Then you can load this model and run inference.
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+# Load pre-trained model and tokenizer
+model_name = "Anvilogic/URLGuardian"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)  # Adjust `num_labels` based on your task
+# Example sentences
+sentences = ["paypal.com.secure-login.xyz","bit.ly/fake-login"]
+# Tokenize inputs
+inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits  # Raw predictions
+    predictions = torch.argmax(logits, dim=-1)  # Convert to class labels
+# Print results
+print(predictions.tolist())  # Example output: [1, 0] (Assuming 1 = Positive, 0 = Negative)
+```
+### Downstream Usage
+This dataset enables real-time malicious URL detection with lightweight models, supporting large-scale inference for phishing prevention and cybersecurity monitoring.
+## Training Details
+
+### Framework Versions
+- Python: 3.10.14
+- Transformers: 4.49.0
+- PyTorch: 2.2.2
+- Tokenizers: 0.20.3
+
+### Training Data
+
+The model was fine-tuned using [Anvilogic/URL-Guardian-Dataset](https://huggingface.co/datasets/Anvilogic/URL-Guardian-Dataset), which contains URL as well as their labels.
+The dataset was filtered and converted to the parquet format for efficient processing.
+
+### Training Procedure
+The model was optimized using [BCELoss](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html)
+
+#### Training Hyperparameters
+- **Model Architecture**: encoder fine-tuned from [distilbert](https://huggingface.co/distilbert/distilbert-base-cased)
+- **Batch Size**: 32
+- **Epochs**: 3
+- **Learning Rate**: 2e-5
+- **Warmup Steps**: 100
+
+
+## Evaluation
+
+In the final evaluation after training, the model achieved the following metrics on the test set:
+
+**Binary Classification Evaluator**
+```json
+Accuracy : 0.9744
+F1 Score : 0.9742
+Precision : 0.9771
+Recall : 0.9712
+Average Precision : 0.9962
+```
+These results indicate the model's high performance in identifying maliciosu URLs, with strong precision and recall scores that make it well-suited for cybersecurity applications.

URLGuardian/config.json

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+{
+  "_name_or_path": "distilbert-base-cased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.0",
+  "vocab_size": 28996
+}

URLGuardian/gitattributes

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URLGuardian/special_tokens_map.json

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URLGuardian/tokenizer_config.json

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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

URLGuardian/training_args.bin

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