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EN
Tf Xlm R Ner 40 Lang
T
Tf Xlm R Ner 40 Lang
Developed by jplu
Multilingual named entity recognition model based on XLM-Roberta-base, supporting entity recognition in 40 languages
Sequence Labeling
Transformers Supports Multiple Languages#Multilingual NER#Cross-lingual Entity Recognition#XLM-R Fine-tuning
Transformers Downloads 969
Release Time : 3/2/2022
Model Overview
This model is a named entity recognition model fine-tuned on 40 languages based on XLM-Roberta-base, capable of recognizing entity types such as locations (LOC), organizations (ORG), and persons (PER)
Model Features
Multilingual Support
Supports named entity recognition in 40 languages, including major European, Asian, and African languages
High Performance
Achieves an average F1 score of 0.87 across 40 languages, with person recognition F1 score reaching 0.91
Based on XLM-Roberta
Utilizes the powerful XLM-Roberta-base model for fine-tuning, featuring excellent cross-lingual representation capabilities
Model Capabilities
Multilingual text processing
Named entity recognition
Cross-lingual entity recognition
Use Cases
Information Extraction
Multilingual News Analysis
Extract person, organization, and location information from news texts in different languages
Accurately identifies key entities in cross-lingual texts
Cross-lingual Document Processing
Process documents containing multiple languages and uniformly extract named entities
Supports entity recognition in 40 languages for unified processing
Knowledge Graph Construction
Multilingual Knowledge Graph
Extract entities from data sources in different languages to build a cross-lingual knowledge graph
Provides consistent entity recognition capabilities to support multilingual knowledge fusion
🚀 XLM-R + NER
This project presents a fine - tuned XLM - Roberta - base model. It is trained on 40 languages from Wikiann as proposed in XTREME. The development of this model is an ongoing process, and the results will be updated whenever an improvement is achieved.
The model covers the following labels:
LOC
ORG
PER
O
📊 Metrics on evaluation set
Average over the 40 languages
Number of documents: 262300
precision recall f1-score support
ORG 0.81 0.81 0.81 102452
PER 0.90 0.91 0.91 108978
LOC 0.86 0.89 0.87 121868
micro avg 0.86 0.87 0.87 333298
macro avg 0.86 0.87 0.87 333298
Afrikaans
Number of documents: 1000
precision recall f1-score support
ORG 0.89 0.88 0.88 582
PER 0.89 0.97 0.93 369
LOC 0.84 0.90 0.86 518
micro avg 0.87 0.91 0.89 1469
macro avg 0.87 0.91 0.89 1469
Arabic
Number of documents: 10000
precision recall f1-score support
ORG 0.83 0.84 0.84 3507
PER 0.90 0.91 0.91 3643
LOC 0.88 0.89 0.88 3604
micro avg 0.87 0.88 0.88 10754
macro avg 0.87 0.88 0.88 10754
Basque
Number of documents: 10000
precision recall f1-score support
LOC 0.88 0.93 0.91 5228
ORG 0.86 0.81 0.83 3654
PER 0.91 0.91 0.91 4072
micro avg 0.89 0.89 0.89 12954
macro avg 0.89 0.89 0.89 12954
Bengali
Number of documents: 1000
precision recall f1-score support
ORG 0.86 0.89 0.87 325
LOC 0.91 0.91 0.91 406
PER 0.96 0.95 0.95 364
micro avg 0.91 0.92 0.91 1095
macro avg 0.91 0.92 0.91 1095
Bulgarian
Number of documents: 1000
precision recall f1-score support
ORG 0.86 0.83 0.84 3661
PER 0.92 0.95 0.94 4006
LOC 0.92 0.95 0.94 6449
micro avg 0.91 0.92 0.91 14116
macro avg 0.91 0.92 0.91 14116
Burmese
Number of documents: 100
precision recall f1-score support
LOC 0.60 0.86 0.71 37
ORG 0.68 0.63 0.66 30
PER 0.44 0.44 0.44 36
micro avg 0.57 0.65 0.61 103
macro avg 0.57 0.65 0.60 103
Chinese
Number of documents: 10000
precision recall f1-score support
ORG 0.70 0.69 0.70 4022
LOC 0.76 0.81 0.78 3830
PER 0.84 0.84 0.84 3706
micro avg 0.76 0.78 0.77 11558
macro avg 0.76 0.78 0.77 11558
Dutch
Number of documents: 10000
precision recall f1-score support
ORG 0.87 0.87 0.87 3930
PER 0.95 0.95 0.95 4377
LOC 0.91 0.92 0.91 4813
micro avg 0.91 0.92 0.91 13120
macro avg 0.91 0.92 0.91 13120
English
Number of documents: 10000
precision recall f1-score support
LOC 0.83 0.84 0.84 4781
PER 0.89 0.90 0.89 4559
ORG 0.75 0.75 0.75 4633
micro avg 0.82 0.83 0.83 13973
macro avg 0.82 0.83 0.83 13973
Estonian
Number of documents: 10000
precision recall f1-score support
LOC 0.89 0.92 0.91 5654
ORG 0.85 0.85 0.85 3878
PER 0.94 0.94 0.94 4026
micro avg 0.90 0.91 0.90 13558
macro avg 0.90 0.91 0.90 13558
Finnish
Number of documents: 10000
precision recall f1-score support
ORG 0.84 0.83 0.84 4104
LOC 0.88 0.90 0.89 5307
PER 0.95 0.94 0.94 4519
micro avg 0.89 0.89 0.89 13930
macro avg 0.89 0.89 0.89 13930
French
Number of documents: 10000
precision recall f1-score support
LOC 0.90 0.89 0.89 4808
ORG 0.84 0.87 0.85 3876
PER 0.94 0.93 0.94 4249
micro avg 0.89 0.90 0.90 12933
macro avg 0.89 0.90 0.90 12933
Georgian
Number of documents: 10000
precision recall f1-score support
PER 0.90 0.91 0.90 3964
ORG 0.83 0.77 0.80 3757
LOC 0.82 0.88 0.85 4894
micro avg 0.84 0.86 0.85 12615
macro avg 0.84 0.86 0.85 12615
German
Number of documents: 10000
precision recall f1-score support
LOC 0.85 0.90 0.87 4939
PER 0.94 0.91 0.92 4452
ORG 0.79 0.78 0.79 4247
micro avg 0.86 0.86 0.86 13638
macro avg 0.86 0.86 0.86 13638
Greek
Number of documents: 10000
precision recall f1-score support
ORG 0.86 0.85 0.85 3771
LOC 0.88 0.91 0.90 4436
PER 0.91 0.93 0.92 3894
micro avg 0.88 0.90 0.89 12101
macro avg 0.88 0.90 0.89 12101
Hebrew
Number of documents: 10000
precision recall f1-score support
PER 0.87 0.88 0.87 4206
ORG 0.76 0.75 0.76 4190
LOC 0.85 0.85 0.85 4538
micro avg 0.83 0.83 0.83 12934
macro avg 0.82 0.83 0.83 12934
Hindi
Number of documents: 1000
precision recall f1-score support
ORG 0.78 0.81 0.79 362
LOC 0.83 0.85 0.84 422
PER 0.90 0.95 0.92 427
micro avg 0.84 0.87 0.85 1211
macro avg 0.84 0.87 0.85 1211
Hungarian
Number of documents: 10000
precision recall f1-score support
PER 0.95 0.95 0.95 4347
ORG 0.87 0.88 0.87 3988
LOC 0.90 0.92 0.91 5544
micro avg 0.91 0.92 0.91 13879
macro avg 0.91 0.92 0.91 13879
Indonesian
Number of documents: 10000
precision recall f1-score support
ORG 0.88 0.89 0.88 3735
LOC 0.93 0.95 0.94 3694
PER 0.93 0.93 0.93 3947
micro avg 0.91 0.92 0.92 11376
macro avg 0.91 0.92 0.92 11376
Italian
Number of documents: 10000
precision recall f1-score support
LOC 0.88 0.88 0.88 4592
ORG 0.86 0.86 0.86 4088
PER 0.96 0.96 0.96 4732
micro avg 0.90 0.90 0.90 13412
macro avg 0.90 0.90 0.90 13412
Japanese
Number of documents: 10000
precision recall f1-score support
ORG 0.62 0.61 0.62 4184
PER 0.76 0.81 0.78 3812
LOC 0.68 0.74 0.71 4281
micro avg 0.69 0.72 0.70 12277
macro avg 0.69 0.72 0.70 12277
Javanese
Number of documents: 100
precision recall f1-score support
ORG 0.79 0.80 0.80 46
PER 0.81 0.96 0.88 26
LOC 0.75 0.75 0.75 40
micro avg 0.78 0.82 0.80 112
macro avg 0.78 0.82 0.80 112
Kazakh
Number of documents: 1000
precision recall f1-score support
ORG 0.76 0.61 0.68 307
LOC 0.78 0.90 0.84 461
PER 0.87 0.91 0.89 367
micro avg 0.81 0.83 0.82 1135
macro avg 0.81 0.83 0.81 1135
Korean
Number of documents: 10000
precision recall f1-score support
LOC 0.86 0.89 0.88 5097
ORG 0.79 0.74 0.77 4218
PER 0.83 0.86 0.84 4014
micro avg 0.83 0.83 0.83 13329
macro avg 0.83 0.83 0.83 13329
Malay
Number of documents: 1000
precision recall f1-score support
ORG 0.87 0.89 0.88 368
PER 0.92 0.91 0.91 366
LOC 0.94 0.95 0.95 354
micro avg 0.91 0.92 0.91 1088
macro avg 0.91 0.92 0.91 1088
Malayalam
Number of documents: 1000
precision recall f1-score support
ORG 0.75 0.74 0.75 347
PER 0.84 0.89 0.86 417
LOC 0.74 0.75 0.75 391
micro avg 0.78 0.80 0.79 1155
macro avg 0.78 0.80 0.79 1155
Marathi
Number of documents: 1000
precision recall f1-score support
PER 0.89 0.94 0.92 394
LOC 0.82 0.84 0.83 457
ORG 0.84 0.78 0.81 339
micro avg 0.85 0.86 0.85 1190
macro avg 0.85 0.86 0.85 1190
Persian
Number of documents: 10000
precision recall f1-score support
PER 0.93 0.92 0.93 3540
LOC 0.93 0.93 0.93 3584
ORG 0.89 0.92 0.90 3370
micro avg 0.92 0.92 0.92 10494
macro avg 0.92 0.92 0.92 10494
Portuguese
Number of documents: 10000
precision recall f1-score support
LOC 0.90 0.91 0.91 4819
PER 0.94 0.92 0.93 4184
ORG 0.84 0.88 0.86 3670
micro avg 0.89 0.91 0.90 12673
macro avg 0.90 0.91 0.90 12673
Russian
Number of documents: 10000
precision recall f1-score support
PER 0.93 0.96 0.95 3574
LOC 0.87 0.89 0.88 4619
ORG 0.82 0.80 0.81 3858
micro avg 0.87 0.88 0.88 12051
macro avg 0.87 0.88 0.88 12051
Spanish
Number of documents: 10000
precision recall f1-score support
PER 0.95 0.93 0.94 3891
ORG 0.86 0.88 0.87 3709
LOC 0.89 0.91 0.90 4553
micro avg 0.90 0.91 0.90 12153
macro avg 0.90 0.91 0.90 12153
Swahili
Number of documents: 1000
precision recall f1-score support
ORG 0.82 0.85 0.83 349
PER 0.95 0.92 0.94 403
LOC 0.86 0.89 0.88 450
micro avg 0.88 0.89 0.88 1202
macro avg 0.88 0.89 0.88 1202
Tagalog
Number of documents: 1000
precision recall f1-score support
LOC 0.90 0.91 0.90 338
ORG 0.83 0.91 0.87 339
PER 0.96 0.93 0.95 350
micro avg 0.90 0.92 0.91 1027
macro avg 0.90 0.92 0.91 1027
Tamil
Number of documents: 1000
precision recall f1-score support
PER 0.90 0.92 0.91 392
ORG 0.77 0.76 0.76 370
LOC 0.82 0.84 0.83 432
micro avg 0.83 0.84 0.83 1194
macro avg 0.83 0.84 0.83 1194
Telugu
Number of documents: 1000
precision recall f1-score support
ORG 0.76 0.75 0.76 374
PER 0.87 0.90 0.89 407
LOC 0.82 0.84 0.83 446
micro avg 0.83 0.84 0.83 1227
macro avg 0.83 0.84 0.83 1227
Thai
Number of documents: 1000
precision recall f1-score support
PER 0.90 0.92 0.91 392
ORG 0.77 0.76 0.76 370
LOC 0.82 0.84 0.83 432
micro avg 0.83 0.84 0.83 1194
macro avg 0.83 0.84 0.83 1194
Turkish
Number of documents: 1000
precision recall f1-score support
ORG 0.86 0.87 0.87 363
PER 0.95 0.93 0.94 398
LOC 0.89 0.90 0.89 429
micro avg 0.90 0.90 0.90 1190
macro avg 0.90 0.90 0.90 1190
Urdu
Number of documents: 1000
precision recall f1-score support
LOC 0.87 0.90 0.88 397
ORG 0.79 0.82 0.80 372
PER 0.94 0.92 0.93 401
micro avg 0.87 0.89 0.88 1170
macro avg 0.87 0.89 0.88 1170
Vietnamese
Number of documents: 1000
precision recall f1-score support
ORG 0.86 0.87 0.87 363
PER 0.95 0.93 0.94 398
LOC 0.89 0.90 0.89 429
micro avg 0.90 0.90 0.90 1190
macro avg 0.90 0.90 0.90 1190
Yoruba
Number of documents: 1000
precision recall f1-score support
PER 0.90 0.92 0.91 392
ORG 0.77 0.76 0.76 370
LOC 0.82 0.84 0.83 432
micro avg 0.83 0.84 0.83 1194
macro avg 0.83 0.84 0.83 1194
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