|
|
--- |
|
|
license: apache-2.0 |
|
|
pipeline_tag: token-classification |
|
|
--- |
|
|
An example of using an ensemble of models is shown in the main.py file |
|
|
|
|
|
Code for this project: https://github.com/Misha24-10/semeval_ner/tree/main |
|
|
|
|
|
In low lavel classification on MultiCoNER II in test set: |
|
|
| Класс | Precision | Recall | F1 | |
|
|
|---------------------------|-----------|--------|--------| |
|
|
| Facility | 0,7464 | 0,7321 | 0,7392 | |
|
|
| OtherLOC | 0,7932 | 0,7068 | 0,7475 | |
|
|
| HumanSettlement | 0,899 | 0,8948 | 0,8969 | |
|
|
| Station | 0,8318 | 0,8125 | 0,8221 | |
|
|
| VisualWork | 0,8528 | 0,8319 | 0,8422 | |
|
|
| MusicalWork | 0,8025 | 0,7813 | 0,7917 | |
|
|
| WrittenWork | 0,7766 | 0,728 | 0,7515 | |
|
|
| ArtWork | 0,6374 | 0,5528 | 0,5921 | |
|
|
| Software | 0,8476 | 0,8201 | 0,8336 | |
|
|
| MusicalGRP | 0,8185 | 0,8207 | 0,8196 | |
|
|
| PublicCorp | 0,7853 | 0,7572 | 0,771 | |
|
|
| PrivateCorp | 0,7362 | 0,6896 | 0,7121 | |
|
|
| AerospaceManufacturer | 0,6774 | 0,7541 | 0,7137 | |
|
|
| SportsGRP | 0,8715 | 0,8938 | 0,8825 | |
|
|
| CarManufacturer | 0,7617 | 0,7902 | 0,7757 | |
|
|
| ORG | 0,7617 | 0,7371 | 0,7492 | |
|
|
| Scientist | 0,5338 | 0,4886 | 0,5102 | |
|
|
| Artist | 0,7971 | 0,8369 | 0,8165 | |
|
|
| Athlete | 0,8094 | 0,802 | 0,8057 | |
|
|
| Politician | 0,7115 | 0,6194 | 0,6622 | |
|
|
| Cleric | 0,7349 | 0,6239 | 0,6748 | |
|
|
| SportsManager | 0,678 | 0,6097 | 0,6421 | |
|
|
| OtherPER | 0,5354 | 0,5915 | 0,562 | |
|
|
| Clothing | 0,6326 | 0,6876 | 0,659 | |
|
|
| Vehicle | 0,6699 | 0,6608 | 0,6653 | |
|
|
| Food | 0,6814 | 0,6634 | 0,6723 | |
|
|
| Drink | 0,6859 | 0,7203 | 0,7027 | |
|
|
| OtherPROD | 0,7033 | 0,6638 | 0,683 | |
|
|
| Medication/Vaccine | 0,7943 | 0,816 | 0,805 | |
|
|
| MedicalProcedure | 0,7481 | 0,7375 | 0,7428 | |
|
|
| AnatomicalStructure | 0,7765 | 0,7567 | 0,7664 | |
|
|
| Symptom | 0,6086 | 0,7178 | 0,6587 | |
|
|
| Disease | 0,7977 | 0,7719 | 0,7846 | |
|
|
| Macro Average Performance | 0,7423 | 0,7294 | 0,7349 | |
|
|
|
|
|
|
|
|
|
|
|
In high lavel classification on MultiCoNER II in test set: |
|
|
| Класс | Precision | Recall | F1 | |
|
|
|---------------------------|-----------|--------|--------| |
|
|
| LOC | 0,8866 | 0,8732 | 0,8798 | |
|
|
| Medicine | 0,794 | 0,7927 | 0,7934 | |
|
|
| GRP | 0,8489 | 0,8419 | 0,8454 | |
|
|
| PROD | 0,7449 | 0,7247 | 0,7347 | |
|
|
| PER | 0,9346 | 0,939 | 0,9368 | |
|
|
| CW | 0,8507 | 0,8162 | 0,8331 | |
|
|
| Macro Average Performance | 0,8433 | 0,8313 | 0,8372 | |
|
|
|
|
|
|
|
|
MultiCoNER II features complex NER in these languages: |
|
|
1. English |
|
|
2. Spanish |
|
|
3. Hindi |
|
|
4. Bangla |
|
|
5. Chinese |
|
|
6. Swedish |
|
|
7. Farsi |
|
|
8. French |
|
|
9. Italian |
|
|
10. Portugese |
|
|
11. Ukranian |
|
|
12. German |
|
|
|
|
|
classification entities in low level between languages overall Macro F1-score: |
|
|
| Язык | F1 | |
|
|
|------|--------| |
|
|
| PT | 0,6872 | |
|
|
| IT | 0,7441 | |
|
|
| UK | 0,7199 | |
|
|
| BN | 0,7320 | |
|
|
| FA | 0,6404 | |
|
|
| ES | 0,7230 | |
|
|
| FR | 0,7289 | |
|
|
| DE | 0,7164 | |
|
|
| EN | 0,7069 | |
|
|
| HI | 0,7544 | |
|
|
| ZH | 0,5899 | |
|
|
| SV | 0,7385 | |
|
|
|
|
|
|
