Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+language:
+- en
+license: mit
+tags:
+- text-classification
+- url-classification
+- bert
+- domain-classification
+pipeline_tag: text-classification
+widget:
+- text: "https://acmewidgets.com"
+- text: "https://store.myshopify.com"
+- text: "https://example.wixsite.com/store"
+- text: "https://business.com"
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: urlbert-url-classifier
+  results:
+  - task:
+      type: text-classification
+      name: URL Classification
+    metrics:
+    - type: accuracy
+      value: 0.99
+      name: Test Accuracy
+    - type: f1
+      value: 0.99
+      name: Test F1 Score
+    - type: precision
+      value: 0.99
+      name: Test Precision
+    - type: recall
+      value: 0.99
+      name: Test Recall
 ---
 
-# Model Card for Model ID
+# URL Classifier
 
-<!-- Provide a quick summary of what the model is/does. -->
+A fine-tuned BERT model for binary classification of URLs as either **platform listings** (e.g., `*.myshopify.com`, `*.wixsite.com`) or **official websites** (e.g., `acmewidgets.com`).
 
+## Model Description
 
+This model is a fine-tuned version of [amahdaouy/DomURLs_BERT](https://huggingface.co/amahdaouy/DomURLs_BERT) trained to distinguish between:
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **LABEL_0 (official_website)**: Direct company/brand websites
+- **LABEL_1 (platform)**: Third-party platform listings (Shopify, Wix, etc.)
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+### Base Model
+- **Architecture**: BERT for Sequence Classification
+- **Base Model**: `amahdaouy/DomURLs_BERT`
+- **Tokenizer**: `CrabInHoney/urlbert-tiny-base-v4`
+
+### Training Configuration
+| Parameter | Value |
+|-----------|-------|
+| **Epochs** | 20 |
+| **Learning Rate** | 2e-5 |
+| **Batch Size** | 32 |
+| **Max Sequence Length** | 64 tokens |
+| **Optimizer** | AdamW |
+| **Weight Decay** | 0.01 |
+| **LR Scheduler** | ReduceLROnPlateau |
+| **Early Stopping** | Patience: 3, Threshold: 0.001 |
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+### Training Data
+- Custom curated dataset of platform and official website URLs
+- Balanced training set with equal representation of both classes
+- Domain-specific preprocessing and data augmentation
+
+## Performance
+
+### Test Set Metrics
+| Metric | Threshold | Achieved |
+|--------|-----------|----------|
+| **Accuracy** | ≥ 0.80 | **≥ 0.99** ✅ |
+| **F1 Score** | ≥ 0.80 | **≥ 0.99** ✅ |
+| **Precision** | ≥ 0.80 | **≥ 0.99** ✅ |
+| **Recall** | ≥ 0.80 | **≥ 0.99** ✅ |
+| **False Positive Rate** | ≤ 0.15 | **< 0.01** ✅ |
+| **False Negative Rate** | ≤ 0.15 | **< 0.01** ✅ |
+
+### Example Predictions
+- `https://acmewidgets.com` → **official_website** (99.98% confidence)
+- `https://store.myshopify.com` → **platform** (75.96% confidence)
+- `https://example.wixsite.com/store` → **platform** (high confidence)
+
+## Usage
+
+### Direct Inference with Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "DiligentAI/urlbert-url-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Classify URL
+url = "https://acmewidgets.com"
+inputs = tokenizer(url, return_tensors="pt", truncation=True, max_length=64)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=1).item()
+    confidence = predictions[0][predicted_class].item()
+
+label_map = {0: "official_website", 1: "platform"}
+print(f"Prediction: {label_map[predicted_class]} ({confidence:.2%})")
+Using Hugging Face Pipeline
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="DiligentAI/urlbert-url-classifier")
+result = classifier("https://store.myshopify.com")
+print(result)
+# [{'label': 'LABEL_1', 'score': 0.7596}]
+Pydantic Integration (Production-Ready)
+from transformers import pipeline
+from pydantic import BaseModel, Field
+from typing import Literal
+
+class URLClassificationResult(BaseModel):
+    url: str
+    label: Literal["official_website", "platform"]
+    confidence: float = Field(..., ge=0.0, le=1.0)
+    
+def classify_url(url: str) -> URLClassificationResult:
+    classifier = pipeline("text-classification", model="DiligentAI/urlbert-url-classifier")
+    result = classifier(url[:64])[0]  # Truncate to max_length
+    
+    label_map = {"LABEL_0": "official_website", "LABEL_1": "platform"}
+    
+    return URLClassificationResult(
+        url=url,
+        label=label_map[result["label"]],
+        confidence=result["score"]
+    )
+Limitations and Bias
+Max URL Length: Model trained on 64-token sequences. Longer URLs are truncated.
+Domain Focus: Optimized for e-commerce and business websites
+Platform Coverage: Best performance on common platforms (Shopify, Wix, etc.)
+Language: Primarily trained on English-language domains
+Edge Cases: May have lower confidence on:
+Uncommon TLDs
+Very short URLs
+Internationalized domain names
+Intended Use
+Primary Use Cases:
+URL filtering and categorization pipelines
+Lead qualification systems
+Web scraping and data collection workflows
+Business intelligence and market research
+Out of Scope:
+Content classification (only URL structure is analyzed)
+Malicious URL detection (use dedicated security models)
+Language detection
+Spam filtering
+Model Card Authors
+DiligentAI Team
+Citation
+@misc{urlbert-classifier-2025,
+  author = {DiligentAI},
+  title = {URL Classifier - Platform vs Official Website Detection},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/DiligentAI/urlbert-url-classifier}}
+}
+License
+MIT License
+Framework Versions
+Transformers: 4.57.0+
+PyTorch: 2.0.0+
+Python: 3.10+
+Training Infrastructure
+Framework: PyTorch + Hugging Face Transformers
+Pipeline Orchestration: DVC (Data Version Control)
+CI/CD: GitHub Actions
+Model Format: Safetensors
+Dependencies: See repository
+Model Versioning
+This model is automatically versioned and deployed via GitHub Actions. Each release includes:
+Model checkpoint (.safetensors)
+Tokenizer configuration
+Label mapping (label_map.json)
+Performance metrics (metrics.json)
+Contact
+For issues, questions, or feedback:
+GitHub: DiligentAI/url-classifier
+Organization: DiligentAI
+
+This model card includes:
+
+1. **Metadata** (YAML front matter) for HuggingFace widgets and indexing
+2. **Model description** and purpose
+3. **Complete training details** from your config
+4. **Performance metrics** with quality gates
+5. **Usage examples** (transformers, pipeline, Pydantic)
+6. **Limitations** and intended use cases
+7. **Citation** and licensing information
+8. **Technical specifications** and dependencies