Fine-grained Named Entity Recognition on MultiCoNER2 2023 (test)
85.67PrecisionAWED-FiNER
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| AWED-FiNERLanguage=Italian, Base Encoder=XLM-RoBERTa2026.01 | 85.67 | 85.98 | 85.83 | |
| AWED-FiNERLanguage=Swedish, Base Encoder=XLM-RoBERTa2026.01 | 85.1 | 84.19 | 84.64 | |
| AWED-FiNERLanguage=French, Base Encoder=XLM-RoBERTa2026.01 | 81.83 | 83.03 | 82.43 | |
| AWED-FiNERLanguage=Portuguese, Base Encoder=XLM-RoBERTa2026.01 | 80.07 | 81.98 | 81.01 | |
| AWED-FiNERLanguage=Ukrainian, Base Encoder=XLM-RoBERTa2026.01 | 79.78 | 81.51 | 80.61 | |
| AWED-FiNERLanguage=Spanish, Base Encoder=XLM-RoBERTa2026.01 | 79.51 | 81.42 | 80.45 | |
| AWED-FiNERLanguage=English, Base Encoder=XLM-RoBERTa2026.01 | 78.29 | 80.94 | 79.59 | |
| AWED-FiNERLanguage=Bengali, Base Encoder=XLM-RoBERTa2026.01 | 77.74 | 79.36 | 78.54 | |
| AWED-FiNERLanguage=Hindi, Base Encoder=XLM-RoBERTa2026.01 | 76.07 | 79.42 | 77.71 | |
| AWED-FiNERLanguage=Farsi, Base Encoder=XLM-RoBERTa2026.01 | 76.05 | 78.86 | 77.43 | |
| AWED-FiNERLanguage=German, Base Encoder=XLM-RoBERTa2026.01 | 74.51 | 76.29 | 75.38 | |
| AWED-FiNERLanguage=Chinese, Base Encoder=XLM-RoBERTa2026.01 | 64.66 | 69.42 | 66.95 |