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Update README.md

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@@ -97,48 +97,50 @@ training_args = Seq2SeqTrainingArguments(
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  ## Training and Validation Metrics
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- Step Training Loss Validation Loss Gen Len
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- 500 21.636200 9.776628 2.001900
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- 1000 10.103400 6.105016 2.077900
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- 1500 6.830800 5.081259 3.811600
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- 2000 6.003100 4.702793 4.237300
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- 2500 5.690200 4.469123 4.700000
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- 3000 5.443100 4.274406 4.808300
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- 3500 5.265300 4.121417 4.749400
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- 4000 5.128500 3.989708 4.782300
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- 4500 5.007200 3.885391 4.805100
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- 5000 4.909600 3.787640 4.874800
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- 5500 4.836000 3.715750 4.855500
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- 6000 4.733000 3.640963 4.962000
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- 6500 4.673500 3.587330 5.011600
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- 7000 4.623800 3.531883 5.068300
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- 7500 4.567400 3.481622 5.108500
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- 8000 4.523200 3.445404 5.092700
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- 8500 4.464000 3.413630 5.132700
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- 9000 4.423100 3.326201 5.211700
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- 9500 4.315700 3.238422 5.200600
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- 10000 4.218200 3.143774 5.288100
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- 10500 4.133600 3.080613 5.202300
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- 11000 4.087700 3.011713 5.271800
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- 11500 4.004300 2.957386 5.178700
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- 12000 3.956700 2.898953 5.209600
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- 12500 3.922800 2.850440 5.210100
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- 13000 3.853400 2.796974 5.171700
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- 13500 3.807900 2.745325 5.281200
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- 14000 3.755700 2.708517 5.223000
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- 14500 3.729300 2.678200 5.210700
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- 15000 3.673600 2.637842 5.230200
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- 15500 3.625400 2.607649 5.264100
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- 16000 3.601100 2.592188 5.129800
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- 16500 3.608200 2.556329 5.215800
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- 17000 3.557900 2.536781 5.162900
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- 17500 3.533500 2.504695 5.206000
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- 18000 3.500000 2.477887 5.211600
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- 18500 3.463600 2.456758 5.201000
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- 19000 3.457100 2.433362 5.210000
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- 19500 3.435400 2.411479 5.197600
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- 20000 3.413300 2.392534 5.221100
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- 20500 3.366100 2.378421 5.165200
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- 21000 3.363500 2.357117 5.187300
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- 21500 3.346500 2.343485 5.193600
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- 22000 3.328300 2.331021 5.183300
 
 
 
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  ## Training and Validation Metrics
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+ Step | Training Loss | Validation Loss | Gen Len
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+ --------|---------------|-----------------|---------
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+ 500 | 21.636200 | 9.776628 | 2.001900
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+ 1000 | 10.103400 | 6.105016 | 2.077900
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+ 1500 | 6.830800 | 5.081259 | 3.811600
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+ 2000 | 6.003100 | 4.702793 | 4.237300
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+ 2500 | 5.690200 | 4.469123 | 4.700000
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+ 3000 | 5.443100 | 4.274406 | 4.808300
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+ 3500 | 5.265300 | 4.121417 | 4.749400
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+ 4000 | 5.128500 | 3.989708 | 4.782300
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+ 4500 | 5.007200 | 3.885391 | 4.805100
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+ 5000 | 4.909600 | 3.787640 | 4.874800
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+ 5500 | 4.836000 | 3.715750 | 4.855500
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+ 6000 | 4.733000 | 3.640963 | 4.962000
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+ 6500 | 4.673500 | 3.587330 | 5.011600
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+ 7000 | 4.623800 | 3.531883 | 5.068300
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+ 7500 | 4.567400 | 3.481622 | 5.108500
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+ 8000 | 4.523200 | 3.445404 | 5.092700
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+ 8500 | 4.464000 | 3.413630 | 5.132700
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+ 9000 | 4.423100 | 3.326201 | 5.211700
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+ 9500 | 4.315700 | 3.238422 | 5.200600
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+ 10000 | 4.218200 | 3.143774 | 5.288100
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+ 10500 | 4.133600 | 3.080613 | 5.202300
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+ 11000 | 4.087700 | 3.011713 | 5.271800
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+ 11500 | 4.004300 | 2.957386 | 5.178700
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+ 12000 | 3.956700 | 2.898953 | 5.209600
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+ 12500 | 3.922800 | 2.850440 | 5.210100
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+ 13000 | 3.853400 | 2.796974 | 5.171700
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+ 13500 | 3.807900 | 2.745325 | 5.281200
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+ 14000 | 3.755700 | 2.708517 | 5.223000
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+ 14500 | 3.729300 | 2.678200 | 5.210700
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+ 15000 | 3.673600 | 2.637842 | 5.230200
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+ 15500 | 3.625400 | 2.607649 | 5.264100
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+ 16000 | 3.601100 | 2.592188 | 5.129800
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+ 16500 | 3.608200 | 2.556329 | 5.215800
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+ 17000 | 3.557900 | 2.536781 | 5.162900
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+ 17500 | 3.533500 | 2.504695 | 5.206000
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+ 18000 | 3.500000 | 2.477887 | 5.211600
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+ 18500 | 3.463600 | 2.456758 | 5.201000
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+ 19000 | 3.457100 | 2.433362 | 5.210000
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+ 19500 | 3.435400 | 2.411479 | 5.197600
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+ 20000 | 3.413300 | 2.392534 | 5.221100
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+ 20500 | 3.366100 | 2.378421 | 5.165200
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+ 21000 | 3.363500 | 2.357117 | 5.187300
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+ 21500 | 3.346500 | 2.343485 | 5.193600
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+ 22000 | 3.328300 | 2.331021 | 5.183300
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