added xlm roberta and changed TryNER to TaNER

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import torch
-from transformers import AutoTokenizer, AutoModelForTokenClassification,pipeline
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 def get_ner_bio(pipe,text):
     tok_text = pipe.tokenizer(text, return_tensors='pt')
@@ -35,11 +35,12 @@ def get_ner(pipe,text,aggregation_strategy="first"):
     return {"text": text, "entities": results} 
 
 ner_models = [
-  "livinNector/TryNER-500",
-  "livinNector/TryNER-1k",
+  "livinNector/TaNER-500",
+  "livinNector/TaNER-1k",
   "livinNector/IndicBERTNER",
   "livinNector/IndicNER",
   "ai4bharat/IndicNER",
+  "livinNector/xlm-roberta-base-ner",
   "livinNector/distilbert-multilingual-base-ner"
 ]
 ner_pipes = [pipeline("token-classification",model) for model in ner_models]
@@ -57,7 +58,7 @@ iface = gr.Interface(
     get_ner_outputs,
     [
         gr.Textbox(value=examples[0][0]),
-        gr.Dropdown(["bio_first","first","max","average"],value=examples[0][1])
+        gr.Dropdown(["bio_first", "first", "max", "average"],value=examples[0][1])
      ],
     [gr.Highlight(label=model) for model in ner_models], 
     description='Named Entity Recongnition Interface Comparing Various Transformer Based NER models for Tamil Language.',