scripts/EMBEDDING_SUMMARY.md

This approach ensures:
- **Consistent character positioning**: Character `t` is always at position 1 relative to other characters
- **Feature invariance**: Features don't interfere with character-to-character relationships
- **Order independence**: Feature order doesn't affect character positioning
- **Better attention patterns**: The model can focus on character relationships without feature interference

## Implementation Success ✅

The improved TagTransformer has been successfully implemented and tested with your example:

```
t ɾ a d ˈ u s e n <V;IND;PRS;3;PL> # t ɾ a d u s k ˈ a m o s <V;SBJV;PRS;1;PL> # <V;SBJV;PRS;3;PL>
```

**Key Results:**
- **26 tokens** processed correctly
- **Features get position 0**: All tags and separators have `Char Pos: 0`
- **Characters get sequential positions**: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
- **Special embeddings work**: Features get different special embeddings than characters
- **Consistent relative distances**: Character `t` is always at position 1 relative to other characters, regardless of feature placement

This implementation successfully addresses the problem described in the paper: **"To avoid such an inconsistency, we propose a simple remedy: We set the positional encoding of features to 0 and only start counting the positions for characters."**