switch model deberta

app.py

@@ -5,12 +5,12 @@ from datetime import datetime
 import gradio as gr
 import chess
 import chess.svg
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+from transformers import DebertaV2ForSequenceClassification, AutoTokenizer, pipeline
 
 token = os.environ['auth_token']
 
 tokenizer = AutoTokenizer.from_pretrained('jrahn/chessv3', use_auth_token=token)
-model = AutoModelForSequenceClassification.from_pretrained('jrahn/chessv3', use_auth_token=token)
+model = DebertaV2ForSequenceClassification.from_pretrained('jrahn/chessv4', use_auth_token=token)
 pipe = pipeline(task="text-classification", model=model, tokenizer=tokenizer)
 
 empty_field = '0'
@@ -58,8 +58,8 @@ def btn_play(inp_fen, inp_move, inp_notation, inp_k):
     board = chess.Board(inp_fen)
     
     if inp_move:
-        if inp_notation == 'UCI': mv = chess.Move.from_uci(inp_move) #board.push_uci(inp_move)
-        elif inp_notation == 'SAN': mv = board.parse_san(inp_move) #chess.Move.from_san(inp_move) #board.push_san(inp_move)
+        if inp_notation == 'UCI': mv = chess.Move.from_uci(inp_move)
+        elif inp_notation == 'SAN': mv = board.parse_san(inp_move)
     else:
         mv = chess.Move.from_uci(predict_move(board.fen(), top_k=inp_k))
     
@@ -76,8 +76,10 @@ def btn_play(inp_fen, inp_move, inp_notation, inp_k):
 with gr.Blocks() as block:
     gr.Markdown(
     '''
-    # Play YoloChess - Policy Network v0.3
-    110M Parameter Transformer (BERT-base architecture) trained for text classification from scratch on expert games in modified FEN notation. 
+    # Play YoloChess - Policy Network v0.4
+    110M Parameter Transformer (DeBERTaV2-base architecture)  
+    - pre-trained (MLM) from scratch on FENs  
+    - fine-tuned for text classification expert games in modified FEN notation.  
     '''
     )
     with gr.Row() as row: