Average scores by model
 |
```checkpoint-34280``` (T5-GenQ-TD-v1) significantly outperforms ```query-gen-msmarco-t5-base-v1``` across all ROUGE metrics. The difference is most notable in ROUGE-2, where ```checkpoint-34280``` achieves 56.24% vs. 15.29% for the baseline model. These results suggest ```checkpoint-34280``` produces more precise and high-overlap text generations. |
Density comparison
 |
```checkpoint-34280``` (T5-GenQ-TD-v1) has strong peaks near 100%, indicating high overlap with reference texts. ```query-gen-msmarco-t5-base-v1``` shows a broader distribution, with peaks at low to mid-range scores (10-40%), suggesting greater variability but lower precision. ROUGE-2 has a high density at 0% for the baseline model, implying many instances with no bigram overlap. |
Histogram comparison
 |
```checkpoint-34280``` (T5-GenQ-TD-v1, blue) shows a steady increase toward high ROUGE scores, peaking at 100%. ```query-gen-msmarco-t5-base-v1``` (orange) has multiple low-score peaks, particularly in ROUGE-2, reinforcing its lower text overlap performance. These histograms confirm that ```checkpoint-34280``` consistently generates more accurate outputs. |
Scores by generated query length
 |
This visualization compares average ROUGE scores and score differences across different word sizes. Consistent ROUGE Scores (Sizes 2-8): ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-LSUM scores remain high and stable across most word sizes. Sharp Drop at Size 9: A significant decrease in scores occurs for size 9 words, with negative score differences, suggesting longer phrases are less aligned with reference texts. Score Differences Stay Near Zero (Sizes 2-8): Models perform similarly for shorter text spans but diverge at larger word sizes. |
Semantic similarity distribution
 |
This histogram visualizes the distribution of cosine similarity scores, which measure the semantic similarity between paired texts (generated query and target query). A strong peak near 1.0 indicates that most pairs are highly semantically similar. Low similarity scores (0.0–0.4) are rare, suggesting the dataset consists mostly of highly related text pairs. |
Semantic similarity score against ROUGE scores
 |
This scatter plot matrix shows the relationship between semantic similarity (cosine similarity) and ROUGE scores: Higher similarity → Higher ROUGE scores, indicating a positive correlation. ROUGE-1 & ROUGE-L show the strongest alignment, while ROUGE-2 has greater variance. Some low-similarity outliers still achieve moderate ROUGE scores, suggesting surface-level overlap without deep semantic alignment. This analysis helps understand how semantic similarity aligns with n-gram overlap metrics for evaluating text models. |