Update README.md

README.md

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 This section outlines the performance of the model on the sentiment analysis task using the IMDb movie reviews dataset, both before and after the fine-tuning process. The results highlight the effectiveness of fine-tuning in enhancing model accuracy and generalization.
 
 Pre-Fine-Tuning Evaluation:
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 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
 
 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:
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 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.