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README.md
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@@ -18,19 +18,25 @@ The primary purpose of this fine-tuned model is to perform sentiment analysis on
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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.
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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:
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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.
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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:
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Loss: 6.091e-06
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Evaluation Runtime: 39.3821 seconds
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Samples per Second: 63.481
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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.
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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.
|
19 |
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Pre-Fine-Tuning Evaluation:
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+
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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:
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Loss: 0.6518
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+
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Evaluation Runtime: 42.6174 seconds
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+
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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.
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Post-Fine-Tuning Evaluation:
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+
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After fine-tuning, the model was re-evaluated on the same dataset to assess improvements from the training adjustments:
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Loss: 6.091e-06
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+
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Evaluation Runtime: 39.3821 seconds
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+
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Samples per Second: 63.481
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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.
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