Update README.md

README.md

@@ -17,16 +17,9 @@ tags:
 ---
 
 # 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). 
+This **Gretel GLiNER** model is a fine-tuned version of the GLiNER base model `knowledgator/gliner-bi-large-v1.0`, specifically trained 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).
+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-large-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.
 
@@ -38,7 +31,7 @@ For detailed statistics on the dataset, including domain and entity type distrib
 
 ### Model Performance
 
-The fine-tuned Gretel GLiNER models demonstrate substantial improvements over their base counterparts in accuracy, precision, recall, and F1 score:
+All 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 |
 |---------------------------------------|----------|-----------|--------|----------|
@@ -61,7 +54,7 @@ Load the fine-tuned Gretel GLiNER model using the GLiNER class and the from_pret
 from gliner import GLiNER
 
 # Load the fine-tuned GLiNER model
-model = GLiNER.from_pretrained("gretelai/gretel-gliner-bi-base-v1.0")
+model = GLiNER.from_pretrained("gretelai/gretel-gliner-bi-large-v1.0")
 
 # Sample text containing PII/PHI entities
 text = """