Update README.md

README.md

@@ -22,8 +22,11 @@ Pre-Fine-Tuning Evaluation:
 Before fine-tuning, the model was evaluated on the IMDb dataset to establish a baseline for its performance. The initial evaluation yielded the following metrics:
 
 Loss: 0.6518
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 Evaluation Runtime: 42.6174 seconds
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 Samples per Second: 58.662
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 These results indicate the model's performance with the original distilbert-base-uncased training, without any adjustments for the specific task of sentiment analysis on movie reviews.
 
 Post-Fine-Tuning Evaluation:
@@ -31,7 +34,9 @@ Post-Fine-Tuning Evaluation:
 After fine-tuning, the model was re-evaluated on the same dataset to assess improvements from the training adjustments:
 
 Loss: 6.091e-06
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 Evaluation Runtime: 39.3821 seconds
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 Samples per Second: 63.481
 The significant reduction in loss demonstrates a substantial increase in the model's accuracy and its capability to correctly classify sentiment in movie reviews. The improvement in processing speed (samples per second) also suggests enhanced efficiency post-fine-tuning.