[ADD] models

app.py

@@ -4,10 +4,12 @@ import gradio as gr
 
 # Available models
 AVAILABLE_MODELS = {
-    "BaoLocTown/gliner-vn-demo": "BaoLocTown/gliner-vn-demo",
     "nvidia/gliner-PII": "nvidia/gliner-PII",
     "urchade/gliner_multi_pii-v1": "urchade/gliner_multi_pii-v1",
+    "BaoLocTown/gliner-vn-demo": "BaoLocTown/gliner-vn-demo",
     "BaoLocTown/gliner-bi-large-v1.0-tuned-v3-50k": "BaoLocTown/gliner-bi-large-v1.0-tuned-v3-50k",
+    "BaoLocTown/gliner_multi_pii-v1-tuned-v4-50k-remapping-all": "BaoLocTown/gliner_multi_pii-v1-tuned-v4-50k-remapping-all",
+    "BaoLocTown/gliner-bi-large-v1.0-tuned-v4-50k-remapping-all": "BaoLocTown/gliner-bi-large-v1.0-tuned-v4-50k-remapping-all",
 }
 
 # Cache for loaded models
@@ -74,10 +76,10 @@ def ner(
     }
 
 
-with gr.Blocks(title="GLiNER-M-v2.1") as demo:
+with gr.Blocks(title="VN-GLiNER") as demo:
     gr.Markdown(
         """
-        # GLiNER-PII (Personnally Identifiable Information extraction)
+        # VN-GLiNER (Vietnamese Named Entity Recognition)
 
         GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
 
@@ -85,8 +87,7 @@ with gr.Blocks(title="GLiNER-M-v2.1") as demo:
         
         ## Links
 
-        * Model: https://huggingface.co/urchade/gliner_multi_pii-v1
-        * All GLiNER models: https://huggingface.co/models?library=gliner
+        * All VN-GLiNER models: https://huggingface.co/collections/BaoLocTown/vn-gliner
         * Paper: https://arxiv.org/abs/2311.08526
         * Repository: https://github.com/urchade/GLiNER
         """