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--- |
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language: |
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- multilingual |
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- af |
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- ar |
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- bg |
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- bn |
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- de |
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- el |
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- en |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fr |
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- he |
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- hi |
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- hu |
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- id |
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- it |
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- ja |
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- jv |
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- ka |
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- kk |
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- ko |
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- ml |
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- mr |
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- ms |
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- my |
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- nl |
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- pt |
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- ru |
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- sw |
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- ta |
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- te |
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- th |
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- tl |
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- tr |
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- ur |
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- vi |
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- yo |
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- zh |
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language_bcp47: |
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- fa-IR |
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--- |
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|
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# XLM-R + NER |
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This model is a fine-tuned [XLM-Roberta-base](https://arxiv.org/abs/1911.02116) over the 40 languages proposed in [XTREME](https://github.com/google-research/xtreme) from [Wikiann](https://aclweb.org/anthology/P17-1178). This is still an on-going work and the results will be updated everytime an improvement is reached. |
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The covered labels are: |
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``` |
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LOC |
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ORG |
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PER |
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O |
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``` |
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## Metrics on evaluation set: |
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### Average over the 40 languages |
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Number of documents: 262300 |
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``` |
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precision recall f1-score support |
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ORG 0.81 0.81 0.81 102452 |
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PER 0.90 0.91 0.91 108978 |
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LOC 0.86 0.89 0.87 121868 |
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micro avg 0.86 0.87 0.87 333298 |
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macro avg 0.86 0.87 0.87 333298 |
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``` |
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### Afrikaans |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.89 0.88 0.88 582 |
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PER 0.89 0.97 0.93 369 |
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LOC 0.84 0.90 0.86 518 |
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micro avg 0.87 0.91 0.89 1469 |
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macro avg 0.87 0.91 0.89 1469 |
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``` |
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### Arabic |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.83 0.84 0.84 3507 |
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PER 0.90 0.91 0.91 3643 |
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LOC 0.88 0.89 0.88 3604 |
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micro avg 0.87 0.88 0.88 10754 |
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macro avg 0.87 0.88 0.88 10754 |
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``` |
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### Basque |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.88 0.93 0.91 5228 |
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ORG 0.86 0.81 0.83 3654 |
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PER 0.91 0.91 0.91 4072 |
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micro avg 0.89 0.89 0.89 12954 |
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macro avg 0.89 0.89 0.89 12954 |
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``` |
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### Bengali |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.86 0.89 0.87 325 |
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LOC 0.91 0.91 0.91 406 |
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PER 0.96 0.95 0.95 364 |
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micro avg 0.91 0.92 0.91 1095 |
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macro avg 0.91 0.92 0.91 1095 |
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``` |
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### Bulgarian |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.86 0.83 0.84 3661 |
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PER 0.92 0.95 0.94 4006 |
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LOC 0.92 0.95 0.94 6449 |
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micro avg 0.91 0.92 0.91 14116 |
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macro avg 0.91 0.92 0.91 14116 |
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``` |
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### Burmese |
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Number of documents: 100 |
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``` |
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precision recall f1-score support |
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LOC 0.60 0.86 0.71 37 |
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ORG 0.68 0.63 0.66 30 |
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PER 0.44 0.44 0.44 36 |
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micro avg 0.57 0.65 0.61 103 |
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macro avg 0.57 0.65 0.60 103 |
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``` |
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### Chinese |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.70 0.69 0.70 4022 |
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LOC 0.76 0.81 0.78 3830 |
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PER 0.84 0.84 0.84 3706 |
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micro avg 0.76 0.78 0.77 11558 |
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macro avg 0.76 0.78 0.77 11558 |
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``` |
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### Dutch |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.87 0.87 0.87 3930 |
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PER 0.95 0.95 0.95 4377 |
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LOC 0.91 0.92 0.91 4813 |
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micro avg 0.91 0.92 0.91 13120 |
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macro avg 0.91 0.92 0.91 13120 |
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``` |
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### English |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.83 0.84 0.84 4781 |
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PER 0.89 0.90 0.89 4559 |
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ORG 0.75 0.75 0.75 4633 |
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micro avg 0.82 0.83 0.83 13973 |
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macro avg 0.82 0.83 0.83 13973 |
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``` |
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### Estonian |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.89 0.92 0.91 5654 |
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ORG 0.85 0.85 0.85 3878 |
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PER 0.94 0.94 0.94 4026 |
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micro avg 0.90 0.91 0.90 13558 |
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macro avg 0.90 0.91 0.90 13558 |
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``` |
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### Finnish |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.84 0.83 0.84 4104 |
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LOC 0.88 0.90 0.89 5307 |
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PER 0.95 0.94 0.94 4519 |
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micro avg 0.89 0.89 0.89 13930 |
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macro avg 0.89 0.89 0.89 13930 |
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``` |
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### French |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.90 0.89 0.89 4808 |
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ORG 0.84 0.87 0.85 3876 |
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PER 0.94 0.93 0.94 4249 |
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micro avg 0.89 0.90 0.90 12933 |
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macro avg 0.89 0.90 0.90 12933 |
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``` |
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### Georgian |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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PER 0.90 0.91 0.90 3964 |
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ORG 0.83 0.77 0.80 3757 |
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LOC 0.82 0.88 0.85 4894 |
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micro avg 0.84 0.86 0.85 12615 |
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macro avg 0.84 0.86 0.85 12615 |
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``` |
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### German |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.85 0.90 0.87 4939 |
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PER 0.94 0.91 0.92 4452 |
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ORG 0.79 0.78 0.79 4247 |
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micro avg 0.86 0.86 0.86 13638 |
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macro avg 0.86 0.86 0.86 13638 |
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``` |
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### Greek |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.86 0.85 0.85 3771 |
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LOC 0.88 0.91 0.90 4436 |
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PER 0.91 0.93 0.92 3894 |
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micro avg 0.88 0.90 0.89 12101 |
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macro avg 0.88 0.90 0.89 12101 |
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``` |
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### Hebrew |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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PER 0.87 0.88 0.87 4206 |
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ORG 0.76 0.75 0.76 4190 |
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LOC 0.85 0.85 0.85 4538 |
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micro avg 0.83 0.83 0.83 12934 |
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macro avg 0.82 0.83 0.83 12934 |
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``` |
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### Hindi |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.78 0.81 0.79 362 |
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LOC 0.83 0.85 0.84 422 |
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PER 0.90 0.95 0.92 427 |
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micro avg 0.84 0.87 0.85 1211 |
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macro avg 0.84 0.87 0.85 1211 |
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``` |
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### Hungarian |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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PER 0.95 0.95 0.95 4347 |
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ORG 0.87 0.88 0.87 3988 |
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LOC 0.90 0.92 0.91 5544 |
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micro avg 0.91 0.92 0.91 13879 |
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macro avg 0.91 0.92 0.91 13879 |
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``` |
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### Indonesian |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.88 0.89 0.88 3735 |
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LOC 0.93 0.95 0.94 3694 |
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PER 0.93 0.93 0.93 3947 |
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micro avg 0.91 0.92 0.92 11376 |
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macro avg 0.91 0.92 0.92 11376 |
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``` |
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### Italian |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.88 0.88 0.88 4592 |
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ORG 0.86 0.86 0.86 4088 |
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PER 0.96 0.96 0.96 4732 |
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micro avg 0.90 0.90 0.90 13412 |
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macro avg 0.90 0.90 0.90 13412 |
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``` |
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### Japanese |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.62 0.61 0.62 4184 |
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PER 0.76 0.81 0.78 3812 |
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LOC 0.68 0.74 0.71 4281 |
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micro avg 0.69 0.72 0.70 12277 |
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macro avg 0.69 0.72 0.70 12277 |
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``` |
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### Javanese |
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Number of documents: 100 |
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``` |
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precision recall f1-score support |
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ORG 0.79 0.80 0.80 46 |
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PER 0.81 0.96 0.88 26 |
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LOC 0.75 0.75 0.75 40 |
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micro avg 0.78 0.82 0.80 112 |
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macro avg 0.78 0.82 0.80 112 |
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``` |
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### Kazakh |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.76 0.61 0.68 307 |
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LOC 0.78 0.90 0.84 461 |
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PER 0.87 0.91 0.89 367 |
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micro avg 0.81 0.83 0.82 1135 |
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macro avg 0.81 0.83 0.81 1135 |
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``` |
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### Korean |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.86 0.89 0.88 5097 |
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ORG 0.79 0.74 0.77 4218 |
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PER 0.83 0.86 0.84 4014 |
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micro avg 0.83 0.83 0.83 13329 |
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macro avg 0.83 0.83 0.83 13329 |
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``` |
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### Malay |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.87 0.89 0.88 368 |
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PER 0.92 0.91 0.91 366 |
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LOC 0.94 0.95 0.95 354 |
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micro avg 0.91 0.92 0.91 1088 |
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macro avg 0.91 0.92 0.91 1088 |
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``` |
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### Malayalam |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.75 0.74 0.75 347 |
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PER 0.84 0.89 0.86 417 |
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LOC 0.74 0.75 0.75 391 |
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micro avg 0.78 0.80 0.79 1155 |
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macro avg 0.78 0.80 0.79 1155 |
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``` |
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### Marathi |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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PER 0.89 0.94 0.92 394 |
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LOC 0.82 0.84 0.83 457 |
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ORG 0.84 0.78 0.81 339 |
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micro avg 0.85 0.86 0.85 1190 |
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macro avg 0.85 0.86 0.85 1190 |
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``` |
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### Persian |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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PER 0.93 0.92 0.93 3540 |
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LOC 0.93 0.93 0.93 3584 |
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ORG 0.89 0.92 0.90 3370 |
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micro avg 0.92 0.92 0.92 10494 |
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macro avg 0.92 0.92 0.92 10494 |
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``` |
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### Portuguese |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.90 0.91 0.91 4819 |
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PER 0.94 0.92 0.93 4184 |
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ORG 0.84 0.88 0.86 3670 |
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micro avg 0.89 0.91 0.90 12673 |
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macro avg 0.90 0.91 0.90 12673 |
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``` |
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### Russian |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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PER 0.93 0.96 0.95 3574 |
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LOC 0.87 0.89 0.88 4619 |
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ORG 0.82 0.80 0.81 3858 |
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micro avg 0.87 0.88 0.88 12051 |
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macro avg 0.87 0.88 0.88 12051 |
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``` |
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### Spanish |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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PER 0.95 0.93 0.94 3891 |
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ORG 0.86 0.88 0.87 3709 |
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LOC 0.89 0.91 0.90 4553 |
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micro avg 0.90 0.91 0.90 12153 |
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macro avg 0.90 0.91 0.90 12153 |
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``` |
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### Swahili |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.82 0.85 0.83 349 |
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PER 0.95 0.92 0.94 403 |
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LOC 0.86 0.89 0.88 450 |
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micro avg 0.88 0.89 0.88 1202 |
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macro avg 0.88 0.89 0.88 1202 |
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``` |
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### Tagalog |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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LOC 0.90 0.91 0.90 338 |
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ORG 0.83 0.91 0.87 339 |
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PER 0.96 0.93 0.95 350 |
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micro avg 0.90 0.92 0.91 1027 |
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macro avg 0.90 0.92 0.91 1027 |
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``` |
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### Tamil |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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PER 0.90 0.92 0.91 392 |
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ORG 0.77 0.76 0.76 370 |
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LOC 0.78 0.81 0.79 421 |
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micro avg 0.82 0.83 0.82 1183 |
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macro avg 0.82 0.83 0.82 1183 |
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``` |
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### Telugu |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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ORG 0.67 0.55 0.61 347 |
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LOC 0.78 0.87 0.82 453 |
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PER 0.73 0.86 0.79 393 |
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micro avg 0.74 0.77 0.76 1193 |
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macro avg 0.73 0.77 0.75 1193 |
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``` |
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### Thai |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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LOC 0.63 0.76 0.69 3928 |
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PER 0.78 0.83 0.80 6537 |
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ORG 0.59 0.59 0.59 4257 |
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micro avg 0.68 0.74 0.71 14722 |
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macro avg 0.68 0.74 0.71 14722 |
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``` |
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### Turkish |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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PER 0.94 0.94 0.94 4337 |
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ORG 0.88 0.89 0.88 4094 |
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LOC 0.90 0.92 0.91 4929 |
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micro avg 0.90 0.92 0.91 13360 |
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macro avg 0.91 0.92 0.91 13360 |
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``` |
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### Urdu |
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Number of documents: 1000 |
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``` |
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precision recall f1-score support |
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LOC 0.90 0.95 0.93 352 |
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PER 0.96 0.96 0.96 333 |
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ORG 0.91 0.90 0.90 326 |
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micro avg 0.92 0.94 0.93 1011 |
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macro avg 0.92 0.94 0.93 1011 |
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``` |
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### Vietnamese |
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Number of documents: 10000 |
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``` |
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precision recall f1-score support |
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ORG 0.86 0.87 0.86 3579 |
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LOC 0.88 0.91 0.90 3811 |
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PER 0.92 0.93 0.93 3717 |
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micro avg 0.89 0.90 0.90 11107 |
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macro avg 0.89 0.90 0.90 11107 |
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``` |
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### Yoruba |
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Number of documents: 100 |
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``` |
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precision recall f1-score support |
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LOC 0.54 0.72 0.62 36 |
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ORG 0.58 0.31 0.41 35 |
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PER 0.77 1.00 0.87 36 |
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micro avg 0.64 0.68 0.66 107 |
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macro avg 0.63 0.68 0.63 107 |
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``` |
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## Reproduce the results |
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Download and prepare the dataset from the [XTREME repo](https://github.com/google-research/xtreme#download-the-data). Next, from the root of the transformers repo run: |
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``` |
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cd examples/ner |
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python run_tf_ner.py \ |
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--data_dir . \ |
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--labels ./labels.txt \ |
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--model_name_or_path jplu/tf-xlm-roberta-base \ |
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--output_dir model \ |
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--max-seq-length 128 \ |
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--num_train_epochs 2 \ |
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--per_gpu_train_batch_size 16 \ |
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--per_gpu_eval_batch_size 32 \ |
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--do_train \ |
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--do_eval \ |
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--logging_dir logs \ |
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--mode token-classification \ |
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--evaluate_during_training \ |
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--optimizer_name adamw |
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``` |
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|
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## Usage with pipelines |
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```python |
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from transformers import pipeline |
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|
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nlp_ner = pipeline( |
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"ner", |
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model="jplu/tf-xlm-r-ner-40-lang", |
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tokenizer=( |
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'jplu/tf-xlm-r-ner-40-lang', |
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{"use_fast": True}), |
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framework="tf" |
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) |
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|
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text_fr = "Barack Obama est né à Hawaï." |
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text_en = "Barack Obama was born in Hawaii." |
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text_es = "Barack Obama nació en Hawai." |
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text_zh = "巴拉克·奧巴馬（Barack Obama）出生於夏威夷。" |
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text_ar = "ولد باراك أوباما في هاواي." |
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|
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nlp_ner(text_fr) |
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#Output: [{'word': '▁Barack', 'score': 0.9894659519195557, 'entity': 'PER'}, {'word': '▁Obama', 'score': 0.9888848662376404, 'entity': 'PER'}, {'word': '▁Hawa', 'score': 0.998701810836792, 'entity': 'LOC'}, {'word': 'ï', 'score': 0.9987035989761353, 'entity': 'LOC'}] |
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nlp_ner(text_en) |
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#Output: [{'word': '▁Barack', 'score': 0.9929141998291016, 'entity': 'PER'}, {'word': '▁Obama', 'score': 0.9930834174156189, 'entity': 'PER'}, {'word': '▁Hawaii', 'score': 0.9986202120780945, 'entity': 'LOC'}] |
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nlp_ner(test_es) |
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#Output: [{'word': '▁Barack', 'score': 0.9944776296615601, 'entity': 'PER'}, {'word': '▁Obama', 'score': 0.9949177503585815, 'entity': 'PER'}, {'word': '▁Hawa', 'score': 0.9987911581993103, 'entity': 'LOC'}, {'word': 'i', 'score': 0.9984861612319946, 'entity': 'LOC'}] |
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nlp_ner(test_zh) |
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#Output: [{'word': '夏威夷', 'score': 0.9988449215888977, 'entity': 'LOC'}] |
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nlp_ner(test_ar) |
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#Output: [{'word': '▁با', 'score': 0.9903655648231506, 'entity': 'PER'}, {'word': 'راك', 'score': 0.9850614666938782, 'entity': 'PER'}, {'word': '▁أوباما', 'score': 0.9850308299064636, 'entity': 'PER'}, {'word': '▁ها', 'score': 0.9477543234825134, 'entity': 'LOC'}, {'word': 'وا', 'score': 0.9428229928016663, 'entity': 'LOC'}, {'word': 'ي', 'score': 0.9319471716880798, 'entity': 'LOC'}] |
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``` |
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