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Model: jplu/tf-xlm-r-ner-40-lang

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jplu/tf-xlm-r-ner-40-lang jplu/tf-xlm-r-ner-40-lang
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pytorch

tf

Contributed by

jplu Julien Plu
8 models

How to use this model directly from the 🤗/transformers library:

			
Copy model
tokenizer = AutoTokenizer.from_pretrained("jplu/tf-xlm-r-ner-40-lang") model = TFAutoModelWithLMHead.from_pretrained("jplu/tf-xlm-r-ner-40-lang")

XLM-R + NER

This model is a fine-tuned XLM-Roberta-base over the 40 languages proposed in XTREME from Wikiann. This is still an on-going work and the results will be updated everytime an improvement is reached.

The covered labels are:

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       0.78      0.81      0.79       421

micro avg       0.82      0.83      0.82      1183
macro avg       0.82      0.83      0.82      1183

Telugu

Number of documents: 1000

           precision    recall  f1-score   support

      ORG       0.67      0.55      0.61       347
      LOC       0.78      0.87      0.82       453
      PER       0.73      0.86      0.79       393

micro avg       0.74      0.77      0.76      1193
macro avg       0.73      0.77      0.75      1193

Thai

Number of documents: 10000

           precision    recall  f1-score   support

      LOC       0.63      0.76      0.69      3928
      PER       0.78      0.83      0.80      6537
      ORG       0.59      0.59      0.59      4257

micro avg       0.68      0.74      0.71     14722
macro avg       0.68      0.74      0.71     14722

Turkish

Number of documents: 10000

           precision    recall  f1-score   support

      PER       0.94      0.94      0.94      4337
      ORG       0.88      0.89      0.88      4094
      LOC       0.90      0.92      0.91      4929

micro avg       0.90      0.92      0.91     13360
macro avg       0.91      0.92      0.91     13360

Urdu

Number of documents: 1000

           precision    recall  f1-score   support

      LOC       0.90      0.95      0.93       352
      PER       0.96      0.96      0.96       333
      ORG       0.91      0.90      0.90       326

micro avg       0.92      0.94      0.93      1011
macro avg       0.92      0.94      0.93      1011

Vietnamese

Number of documents: 10000

           precision    recall  f1-score   support

      ORG       0.86      0.87      0.86      3579
      LOC       0.88      0.91      0.90      3811
      PER       0.92      0.93      0.93      3717

micro avg       0.89      0.90      0.90     11107
macro avg       0.89      0.90      0.90     11107

Yoruba

Number of documents: 100

           precision    recall  f1-score   support

      LOC       0.54      0.72      0.62        36
      ORG       0.58      0.31      0.41        35
      PER       0.77      1.00      0.87        36

micro avg       0.64      0.68      0.66       107
macro avg       0.63      0.68      0.63       107

Reproduce the results

Download and prepare the dataset from the XTREME repo. Next, from the root of the transformers repo run:

cd examples/ner
python run_tf_ner.py \
--data_dir . \
--labels ./labels.txt \
--model_name_or_path jplu/tf-xlm-roberta-base \
--output_dir model \
--max-seq-length 128 \
--num_train_epochs 2 \
--per_gpu_train_batch_size 16 \
--per_gpu_eval_batch_size 32 \
--do_train \
--do_eval \
--logging_dir logs \
--mode token-classification \
--evaluate_during_training \
--optimizer_name adamw

Usage with pipelines

from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="jplu/tf-xlm-r-ner-40-lang",
    tokenizer=(
        'jplu/tf-xlm-r-ner-40-lang',  
        {"use_fast": True}),
    framework="tf"
)

text_fr = "Barack Obama est né à Hawaï."
text_en = "Barack Obama was born in Hawaii."
text_es = "Barack Obama nació en Hawai."
text_zh = "巴拉克·奧巴馬(Barack Obama)出生於夏威夷。"
text_ar = "ولد باراك أوباما في هاواي."

nlp_ner(text_fr)
#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'}]
nlp_ner(text_en)
#Output: [{'word': '▁Barack', 'score': 0.9929141998291016, 'entity': 'PER'}, {'word': '▁Obama', 'score': 0.9930834174156189, 'entity': 'PER'}, {'word': '▁Hawaii', 'score': 0.9986202120780945, 'entity': 'LOC'}]
nlp_ner(test_es)
#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'}]
nlp_ner(test_zh)
#Output: [{'word': '夏威夷', 'score': 0.9988449215888977, 'entity': 'LOC'}]
nlp_ner(test_ar)
#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'}]