Update README.md

README.md

@@ -9,6 +9,46 @@ Obviously, it's still not perfect (I won't lie, the original dataset was very fl
 
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+## Token Distribution Analysis
+
+We analyzed the token count distribution of the dataset using the LLAMA-3 tokenizer. This information is crucial for understanding the length of prompts and planning model training.
+
+### Key Statistics
+- **Minimum tokens**: 164
+- **Maximum tokens**: 3,285
+- **Median (50th percentile)**: 274 tokens
+
+### Decile Distribution
+
+```ascii
+| Percentile | Token Count |
+|------------|-------------|
+| 10%        | 192         |
+| 20%        | 209         |
+| 30%        | 228         |
+| 40%        | 249         |
+| 50%        | 274         |
+| 60%        | 302         |
+| 70%        | 337         |
+| 80%        | 386         |
+| 90%        | 467         |
+| 100%       | 3,285       |
+```
+
+### Interpretation
+
+1. **Range**: The dataset contains prompts ranging from 164 to 3,285 tokens, indicating significant variation in prompt lengths.
+
+2. **Central Tendency**: The median token count is 274, meaning half of the prompts have 274 tokens or fewer.
+
+3. **Distribution**: 
+   - 90% of prompts have 467 tokens or fewer.
+   - There's a notable jump from the 90th percentile (467 tokens) to the maximum (3,285 tokens), suggesting some outliers with very high token counts.
+
+4. **Implications for Training**:
+   - A sequence length of 400-500 tokens would cover the majority of prompts.
+   - Special handling may be needed for outliers with high token counts (e.g., truncation or splitting).
+
 ## Acknowledgment and citation
 ```bibtex
 @inproceedings{bien-etal-2020-recipenlg,