Update README.md

README.md

@@ -1,10 +1,10 @@
---
+---
 license: apache-2.0
 language:
-  - english
+  - en
 library_name: gliner
 datasets:
-  - gretel/synthetic_pii_docs_multidomain_en
+  - gretelai/gretel-pii-masking-en-v1
 pipeline_tag: token-classification
 tags:
   - PII
@@ -14,4 +14,147 @@ tags:
   - encoder
   - entity recognition
   - privacy
----
+---
+
+# Gretel GLiNER: Fine-Tuned Models for PII/PHI Detection
+**Gretel GLiNER** is a specialized version of the GLiNER model, fine-tuned specifically for the detection of Personally Identifiable Information (PII) and Protected Health Information (PHI). 
+Gretel GLiNER helps to provide privacy-compliant entity recognition across various industries and document types.
+We have fine-tuned the following GLiNER base models dataset to create specialized models for PII/PHI detection. 
+
+- **Fine-Tuned Models:**
+  - `gretelai/gretel-gliner-bi-small-v1.0`
+  - `gretelai/gretel-gliner-bi-base-v1.0`
+  - `gretelai/gretel-gliner-bi-large-v1.0`
+
+For more information about the base GLiNER model, including its architecture and general capabilities, please refer to the [GLiNER Model Card](https://huggingface.co/knowledgator/gliner-bi-base-v1.0).
+
+The model was fine-tuned on the `gretelai/gretel-pii-masking-en-v1` dataset, which provides a rich and diverse collection of synthetic document snippets containing PII and PHI entities.
+
+1. **Training:** Utilized the training split of the synthetic dataset.
+2. **Validation:** Monitored performance using the validation set to adjust training parameters.
+3. **Evaluation:** Assessed final performance on the test set using PII/PHI entity annotations as ground truth.
+
+For detailed statistics on the dataset, including domain and entity type distributions, visit the [dataset documentation on Hugging Face](https://huggingface.co/datasets/gretel/gretel-pii-masking-en-v1).
+
+### Model Performance
+
+The fine-tuned Gretel GLiNER models demonstrate substantial improvements over their base counterparts in accuracy, precision, recall, and F1 score:
+
+| Model                                 | Accuracy | Precision | Recall | F1 Score |
+|---------------------------------------|----------|-----------|--------|----------|
+| gretelai/gretel-gliner-bi-small-v1.0   | 0.89     | 0.98      | 0.91   | 0.94     |
+| gretelai/gretel-gliner-bi-base-v1.0    | 0.91     | 0.98      | 0.92   | 0.95     |
+| gretelai/gretel-gliner-bi-large-v1.0   | 0.91     | 0.99      | 0.93   | 0.95     |
+
+
+## Installation & Usage
+
+Ensure you have Python installed. Then, install or update the `gliner` package:
+
+```bash
+pip install gliner -U
+```
+
+Load the fine-tuned Gretel GLiNER model using the GLiNER class and the from_pretrained method. Below is an example using the gretelai/gretel-gliner-bi-base-v1.0 model for PII/PHI detection:
+
+```python
+from gliner import GLiNER
+
+# Load the fine-tuned GLiNER model
+model = GLiNER.from_pretrained("gretelai/gretel-gliner-bi-base-v1.0")
+
+# Sample text containing PII/PHI entities
+text = """
+"""
+
+# Define the labels for PII/PHI entities
+labels = [
+    "medical_record_number",
+    "date_of_birth",
+    "ssn",
+    "date",
+    "first_name",
+    "email",
+    "last_name",
+    "customer_id",
+    "employee_id",
+    "name",
+    "street_address",
+    "phone_number",
+    "ipv4",
+    "credit_card_number",
+    "license_plate",
+    "address",
+    "user_name",
+    "device_identifier",
+    "bank_routing_number",
+    "date_time",
+    "company_name",
+    "unique_identifier",
+    "biometric_identifier",
+    "account_number",
+    "city",
+    "certificate_license_number",
+    "time",
+    "postcode",
+    "vehicle_identifier",
+    "coordinate",
+    "country",
+    "api_key",
+    "ipv6",
+    "password",
+    "health_plan_beneficiary_number",
+    "national_id",
+    "tax_id",
+    "url",
+    "state",
+    "swift_bic",
+    "cvv",
+    "pin"
+]
+
+# Predict entities with a confidence threshold of 0.3
+entities = model.predict_entities(text, labels, threshold=0.3)
+
+# Display the detected entities
+for entity in entities:
+    print(f"{entity['text']} => {entity['label']}")
+```
+
+Expected Output:
+
+
+```
+John Doe => first_name
+123-45-6789 => ssn
+2023-04-15 => date
+MRN-987654321 => medical_record_number
+[email protected] => email
+```
+
+## Use Cases
+
+Gretel GLiNER is ideal for applications requiring precise detection and redaction of sensitive information:
+
+- Healthcare: Automating the extraction and redaction of patient information from medical records.
+- Finance: Identifying and securing financial data such as account numbers and transaction details.
+- Cybersecurity: Detecting sensitive information in logs and security reports.
+- Legal: Processing contracts and legal documents to protect client information.
+- Data Privacy Compliance: Ensuring data handling processes adhere to regulations like GDPR and HIPAA by accurately identifying PII/PHI.
+
+## Citation and Usage
+
+If you use this dataset in your research or applications, please cite it as:
+
+```bibtex
+@dataset{gretel-pii-masking-en-v1,
+  author       = {Gretel AI},
+  title        = {GLiNER Models for PII Detection through Fine-Tuning on Gretel-Generated Synthetic Documents},
+  year         = {2024},
+  month        = {10},
+  publisher    = {Gretel},
+  howpublished = {https://huggingface.co/gretelai/gretel-pii-masking-en-v1}
+}
+```
+
+For questions, issues, or additional information, please visit our [Synthetic Data Discord](https://gretel.ai/discord) community or reach out to [gretel.ai](https://gretel.ai/).