| # Recent Updates and Fixes | |
| ## Overview | |
| Two important fixes have been implemented based on testing feedback: | |
| 1. **Leetspeak Translation** (before NER) | |
| 2. **Improved Country Mapping** (check ALL tags) | |
| --- | |
| ## Fix 1: Leetspeak Translation | |
| ### Problem | |
| Names with leetspeak (numbers replacing letters) weren't being properly cleaned: | |
| - `4kira` should be `Akira` | |
| - `1rene` should be `Irene` | |
| - `3mma` should be `Emma` | |
| ### Solution | |
| Added leetspeak translation **before** other NER processing in Cell 5. | |
| ### Mapping Table | |
| | Leetspeak | Letter | | |
| |-----------|--------| | |
| | 4 | A | | |
| | 3 | E | | |
| | 1 | I | | |
| | 0 | O | | |
| | 7 | T | | |
| | 5 | S | | |
| | 8 | B | | |
| | 9 | G | | |
| | @ | A | | |
| | $ | S | | |
| | ! | I | | |
| ### Examples | |
| ``` | |
| 4kira -> akira | |
| 3mma -> emma | |
| 1rene -> irene | |
| L3vi -> Levi | |
| S4sha -> Sasha | |
| K4te -> Kate | |
| J3ssica -> Jessica | |
| ``` | |
| ### Implementation | |
| The `translate_leetspeak()` function runs FIRST in `clean_name()`, before emoji removal and other cleaning steps. This ensures leetspeak is converted to proper letters before any other processing. | |
| --- | |
| ## Fix 2: Improved Country Mapping | |
| ### Problem | |
| The country mapping was stopping at the first match, which meant: | |
| - **Irene** with tags `['girl', 'photorealistic', 'asian', 'woman', 'beautiful', 'celebrity', 'korean']` | |
| - The `'korean'` tag wasn't being properly mapped to `'South Korea'` | |
| - This resulted in incomplete hints being sent to the LLM | |
| - **Expected**: Deepseek should identify **Bae Joo-hyun (Irene)** from Red Velvet | |
| ### Solution | |
| Updated Cell 7 to: | |
| 1. **Check ALL tags** (not just stop at first match) | |
| 2. **Use a priority system** to select the best match: | |
| - Priority 3: Exact country name match (highest) | |
| - Priority 2: Nationality match (medium) | |
| - Priority 1: Word parts (lowest) | |
| ### How It Works | |
| #### Before (Broken) | |
| ```python | |
| def infer_country_and_nationality(tags): | |
| for tag in tags: | |
| if tag in mapping: | |
| return mapping[tag] # ❌ Stops at first match! | |
| return ("", "") | |
| ``` | |
| #### After (Fixed) | |
| ```python | |
| def infer_country_and_nationality(tags): | |
| best_match = None | |
| best_priority = 0 | |
| for tag in tags: # ✅ Check ALL tags | |
| if tag in mapping: | |
| country, nationality, priority = mapping[tag] | |
| if priority > best_priority: | |
| best_match = (country, nationality) | |
| best_priority = priority | |
| return best_match or ("", "") | |
| ``` | |
| ### Example: Irene Case | |
| **Input Tags**: `['girl', 'photorealistic', 'asian', 'woman', 'beautiful', 'celebrity', 'korean']` | |
| **Processing**: | |
| 1. Check `'girl'` → no match | |
| 2. Check `'photorealistic'` → no match | |
| 3. Check `'asian'` → no match (too generic) | |
| 4. Check `'woman'` → no match | |
| 5. Check `'beautiful'` → no match | |
| 6. Check `'celebrity'` → no match | |
| 7. Check `'korean'` → ✅ **MATCH!** | |
| - Maps to nationality: `'South Korean'` | |
| - Which maps to country: `'South Korea'` | |
| - Priority: 2 (nationality match) | |
| **Output**: | |
| - `likely_country`: `'South Korea'` | |
| - `likely_nationality`: `'South Korean'` | |
| **Sent to Deepseek**: | |
| ``` | |
| Given 'Irene' (celebrity, South Korea), provide: | |
| 1. Full legal name | |
| 2. Aliases | |
| 3. Gender | |
| 4. Top 3 professions | |
| 5. Country | |
| ``` | |
| **Expected Result**: Deepseek can now identify this as **Bae Joo-hyun (Irene)**, a South Korean singer/actress from the K-pop group Red Velvet. | |
| --- | |
| ## Impact on Results | |
| ### Better Name Recognition | |
| - Leetspeak names are now properly translated | |
| - LLMs receive cleaner, more recognizable names | |
| ### Better Country Context | |
| - All tags are now considered for country mapping | |
| - More accurate country/nationality hints sent to LLMs | |
| - Better identification of international celebrities | |
| ### Example Improvements | |
| | Name | Tags | Before | After | | |
| |------|------|--------|-------| | |
| | `4kira LoRA` | `['japanese', 'actress']` | `'4kira'` + no country | `'Akira'` + `'Japan'` | | |
| | `Irene` | `['korean', 'celebrity']` | `'Irene'` + no country | `'Irene'` + `'South Korea'` | | |
| | `1U` | `['korean', 'singer']` | `'1U'` + no country | `'IU'` + `'South Korea'` | | |
| | `3lsa` | `['model']` | `'3lsa'` + no country | `'Elsa'` + country if tagged | | |
| --- | |
| ## Testing Recommendations | |
| ### Before Running Full Pipeline | |
| 1. **Test Leetspeak Translation** (Cell 5): | |
| ```python | |
| # Look for names with numbers in the output | |
| # Verify they're properly translated | |
| ``` | |
| 2. **Test Country Mapping** (Cell 7): | |
| ```python | |
| # Check the debug output at the end: | |
| # "🔍 Checking 'Irene' entries:" | |
| # Verify country is properly mapped | |
| ``` | |
| 3. **Test Deepseek Results** (Cell 10): | |
| ```python | |
| # Look for Irene in the results | |
| # Should now identify as Bae Joo-hyun | |
| ``` | |
| ### Validation Checklist | |
| - [ ] Leetspeak names are translated (check console output in Cell 5) | |
| - [ ] Country mapping shows high success rate (check stats in Cell 7) | |
| - [ ] Irene is correctly identified as Bae Joo-hyun (check results in Cell 10) | |
| - [ ] Other K-pop/Korean celebrities are properly identified | |
| - [ ] Japanese/Chinese celebrities also benefit from better country mapping | |
| --- | |
| ## Notes | |
| ### Why Check ALL Tags? | |
| Some entries have many tags, and the most informative tag might not be first: | |
| ``` | |
| tags = ['girl', 'sexy', 'beautiful', 'asian', 'korean', 'celebrity', 'kpop'] | |
| ^^^^ Most informative! | |
| ``` | |
| The old code might stop at `'girl'` or `'asian'` (no country info), missing the `'korean'` tag. | |
| ### Why Use Priority? | |
| Some tags might match multiple countries. Priority ensures we get the best match: | |
| - `'american'` → exact nationality match (priority 2) → USA | |
| - `'america'` → could be North/South/Central America (priority 1) | |
| The system picks the higher priority match. | |
| ### Word Length Filter | |
| Word parts only match if >4 characters to avoid false positives: | |
| - ✅ `'china'` → matches China (5 chars) | |
| - ❌ `'us'` → too short, might be part of other words | |
| --- | |
| ## Future Improvements | |
| Potential enhancements: | |
| 1. **More leetspeak patterns**: `|\/|` for M, `(_)` for U, etc. | |
| 2. **Fuzzy country matching**: Handle typos like `'corean'` → `'korean'` | |
| 3. **Multi-country support**: Some celebrities work in multiple countries | |
| 4. **Language detection**: Use name structure to infer origin | |
| --- | |
| ## Summary | |
| ✅ **Leetspeak translation** ensures names are readable before NER | |
| ✅ **ALL tags checked** ensures no country hints are missed | |
| ✅ **Priority system** ensures best match is selected | |
| ✅ **Better LLM results** from improved name quality and country context | |
| These fixes should significantly improve the accuracy of person identification, especially for: | |
| - International celebrities (K-pop, J-pop, C-pop) | |
| - Names with leetspeak | |
| - Entries where country info appears later in tag list | |