## Heatmap: Baseline - Core Generalization - Qwen-2.5 7B ### Overview This image presents a heatmap visualizing the accuracy of a model (Qwen-2.5 7B) across different sequence lengths and data types. The heatmap uses a color gradient to represent accuracy percentages, ranging from approximately 20% (lightest shade) to 100% (darkest shade). The heatmap is structured with 'Length' on the x-axis and 'Type' on the y-axis. ### Components/Axes * **Title:** Baseline - Core Generalization - Qwen-2.5 7B (Top-center) * **X-axis Label:** Length (Bottom-center) * **X-axis Markers:** 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 * **Y-axis Label:** Type (Left-center) * **Y-axis Markers:** '–', '~', 'm', '4', 'u', '>' * **Color Scale:** A vertical color bar on the right side represents accuracy in percentage (%). The scale ranges from 0% to 100%. * **Data Cells:** Each cell in the heatmap represents the accuracy for a specific combination of 'Length' and 'Type'. The cells are colored according to the accuracy percentage. ### Detailed Analysis The heatmap displays accuracy values for six different 'Type' categories across sequence lengths from 0 to 19. I will analyze each 'Type' row individually, noting trends and specific values. * **Type '–'**: Accuracy is consistently high, ranging from approximately 99.0% to 100.0% across all lengths. * **Type '~'**: Accuracy is also very high, ranging from approximately 99.3% to 100.0% across all lengths. * **Type 'm'**: Accuracy is high, ranging from approximately 98.6% to 99.0% for lengths 0-4, then slightly decreases to around 90.4% - 98.7% for lengths 5-19. * **Type '4'**: Accuracy starts at approximately 98.7% for length 0, decreases to around 94.7% for length 5, and then increases again to approximately 91.7% for length 19. * **Type 'u'**: Accuracy starts at approximately 91.0% for length 6, and increases to approximately 88.7% for length 18, and then increases to approximately 88.0% for length 19. * **Type '>'**: Accuracy is high for shorter lengths (0-4), ranging from approximately 98.3% to 100.0%. It then decreases to approximately 87.7% for length 19. Here's a more detailed breakdown of specific values (approximate): | Length | Type '–' | Type '~' | Type 'm' | Type '4' | Type 'u' | Type '>' | |---|---|---|---|---|---|---| | 0 | 100.0 | 100.0 | 99.0 | 98.7 | 91.0 | 100.0 | | 1 | 99.7 | 99.3 | 98.7 | 97.3 | 94.7 | 99.8 | | 2 | 99.5 | 99.0 | 98.6 | 96.3 | 94.7 | 99.0 | | 3 | 99.0 | 98.7 | 98.6 | 95.3 | 94.0 | 98.7 | | 4 | 98.7 | 98.7 | 98.6 | 93.0 | 94.7 | 98.0 | | 5 | 98.0 | 99.7 | 97.3 | 94.7 | 94.0 | 97.8 | | 6 | 97.7 | 99.3 | 94.0 | 94.7 | 93.3 | 96.7 | | 7 | 97.3 | 98.7 | 92.7 | 94.7 | 93.3 | 94.0 | | 8 | 97.0 | 98.7 | 90.7 | 88.7 | 88.3 | 94.0 | | 9 | 96.7 | 98.7 | 90.7 | 88.3 | 88.7 | 93.0 | | 10 | 96.0 | 100.0 | 90.4 | 96.0 | 90.3 | 90.0 | | 11 | 95.7 | 99.7 | 91.3 | 94.7 | 90.3 | 89.0 | | 12 | 95.3 | 99.7 | 91.3 | 94.0 | 93.3 | 88.7 | | 13 | 95.0 | 99.7 | 93.3 | 93.3 | 93.3 | 88.0 | | 14 | 94.7 | 99.7 | 94.3 | 90.3 | 88.7 | 88.0 | | 15 | 94.7 | 99.7 | 94.7 | 90.3 | 88.7 | 88.0 | | 16 | 94.7 | 100.0 | 95.0 | 92.0 | 88.0 | 88.0 | | 17 | 94.7 | 100.0 | 92.0 | 88.7 | 88.0 | 88.0 | | 18 | 90.7 | 100.0 | 92.0 | 88.0 | 88.0 | 88.0 | | 19 | 90.7 | 100.0 | 92.0 | 91.7 | 88.0 | 87.7 | ### Key Observations * The model consistently performs very well (accuracy > 95%) for 'Type' '–' and '~' across all sequence lengths. * 'Type' 'm' shows a slight decrease in accuracy as the sequence length increases, but remains relatively high. * 'Type' '4' exhibits a more pronounced decrease in accuracy with increasing sequence length, followed by a slight increase towards the end. * 'Type' 'u' shows a consistent decrease in accuracy with increasing sequence length. * 'Type' '>' shows a decrease in accuracy with increasing sequence length. * The heatmap reveals that the model's performance is sensitive to both the type of data and the sequence length. ### Interpretation This heatmap demonstrates the generalization capabilities of the Qwen-2.5 7B model across different data types and sequence lengths. The consistently high accuracy for 'Type' '–' and '~' suggests that the model is well-suited for these types of data. The decreasing accuracy for 'Type' '4', 'u', and '>' as sequence length increases indicates that the model may struggle with longer sequences for these data types. This could be due to limitations in the model's ability to capture long-range dependencies or to the presence of more complex patterns in these data types. The heatmap provides valuable insights into the model's strengths and weaknesses, which can be used to guide further development and optimization efforts. The variations in performance across different types suggest that the model may benefit from type-specific fine-tuning or data augmentation strategies. The heatmap is a useful tool for understanding the model's behavior and identifying areas for improvement.