---
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 |