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fine-tuned-distilbert-base-uncased

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naveedui commited on May 14

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@@ -18,26 +18,21 @@ The primary purpose of this fine-tuned model is to perform sentiment analysis on
 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:
 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
 Evaluation Runtime: 42.6174 seconds
 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:
 After fine-tuning, the model was re-evaluated on the same dataset to assess improvements from the training adjustments:
 Loss: 6.091e-06
 Evaluation Runtime: 39.3821 seconds
 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.
 Discussion:

 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:
 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
 Evaluation Runtime: 42.6174 seconds
 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:
 After fine-tuning, the model was re-evaluated on the same dataset to assess improvements from the training adjustments:
 Loss: 6.091e-06
 Evaluation Runtime: 39.3821 seconds
 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.
 Discussion: