# Instance-Level Weighted Contrast Learning for Text Classification
## 4 Experiment
### Ablation Study  
In this study, we design two ablation models, one of which involves (1) removing data augmentation and the other involves (2) removing contrastive learning, and compare them with the complete proposed model to analyze the contribution of each key module to the classification effect. By comparing the performance of each model on three datasets, we can systematically evaluate the importance of data augmentation and contrastive learning in improving text classification performance. The ablation results for the three datasets are shown in Figures 3-5:  
Figure 4: Ablation experiments on takeout datasets.  
Figure 3: Ablation experiments on food datasets.  
(1) Ours (proposed model):  
This model is a complete framework that combines a contrastive learning pre-training model, data augmentation, and instance clustering, aiming to improve text classification performance. The model shows significant advantages on the food dataset, achieving high accuracy, precision, recall, and F1 value. Although the performance on the movie and takeaway datasets is not as good as the food dataset, it still achieves relatively good results, verifying the effectiveness and generalization ability of the model.  
(2) Remove data augmentation:  
This ablation model removes the data enhancement strategy based on the proposed model. Data enhancement plays a key role in contrastive learning. It helps to improve the generalization ability of the model by introducing noisy negative samples in positive samples and optimizing them. The experimental results show that after removing data enhancement, the classification performance of the model on various datasets decreases significantly. On the food dataset, the accuracy (ACC) after removing data enhancement is 0.5198, the precision (Precision) is 0.5131, the recall rate (Recall) is 0.5178, and the F1 value is 0.482, which is significantly lower than the proposed model. On the movie and takeaway datasets, removing data enhancement also leads to similar performance degradation, further proving the importance of data enhancement on the model effects.  
(3) Remove contrastive learning:  
This ablation model removes the contrastive learning part based on the proposed model and directly uses the BERT pre-trained parameters for text classification. Contrastive learning helps to learn more semantically meaningful text representations by introducing positive sample enhancement and optimizing noisy negative samples similarly to positive samples. The experimental results show that after removing contrastive learning, the classification performance of the model also shows a significant decline. On the food dataset, the accuracy (ACC) after removing contrastive learning is 0.4143, the precision (Precision) is 0.6089, the recall rate (Recall) is 0.5942, and the F1 value is 0.4131, which is significantly lower than the proposed model. On the movie and takeaway datasets, the model without contrastive learning also shows similar performance degradation.  
In summary, the experimental results show that the proposed contrastive learning text classification pre-trained model performs better than other models on these datasets, and data augmentation and contrastive learning are the key factors to improve the model performance. Removing these two important parts will lead to a significant decrease in the  
Figure 5: Ablation experiments on film datasets.  
model classification performance, which further verifies the effectiveness and advantages of our proposed method.