Update README.md

README.md

@@ -1,602 +1,9 @@
 ---
 language: multilingual
 ---
+---
+license: MIT
+---
 
-# XLM-R + NER
-
-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. 
-
-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](https://github.com/google-research/xtreme#download-the-data). 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
-```python
-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'}]
+## Extract names in any language.
 
-```