đ XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Dutch
This model addresses the part - of - speech tagging task across multiple languages, offering high - accuracy results. It is a significant contribution in the field of cross - lingual transfer, as demonstrated in the related research paper.
đ Quick Start
This model is part of our paper called:
- Make the Best of Cross - lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the Space for more details.
đģ Usage Examples
Basic Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-nl")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-nl")
đ License
The model is licensed under the Apache - 2.0 license.
đ Model Information
| Property |
Details |
| Model Type |
xlm - roberta - base - ft - udpos28 - nl |
| Training Data |
Universal Dependencies v2.8 |
| Tags |
part - of - speech, token - classification |
| Metrics |
accuracy |
đ Model Results
The model xlm - roberta - base - ft - udpos28 - nl has the following performance metrics on different languages:
| Language |
Test Accuracy |
| English |
88.8 |
| Dutch |
97.0 |
| German |
89.0 |
| Italian |
89.9 |
| French |
88.1 |
| Spanish |
90.5 |
| Russian |
89.2 |
| Swedish |
90.7 |
| Norwegian |
87.6 |
| Danish |
89.0 |
| Low Saxon |
58.3 |
| Akkadian |
22.9 |
| Armenian |
86.7 |
| Welsh |
70.2 |
| Old East Slavic |
73.5 |
| Albanian |
78.9 |
| Slovenian |
76.3 |
| Guajajara |
22.1 |
| Kurmanji |
78.3 |
| Turkish |
78.3 |
| Finnish |
86.2 |
| Indonesian |
85.4 |
| Ukrainian |
85.8 |
| Polish |
86.3 |
| Portuguese |
90.0 |
| Kazakh |
83.0 |
| Latin |
79.0 |
| Old French |
53.1 |
| Buryat |
58.4 |
| Kaapor |
13.8 |
| Korean |
62.2 |
| Estonian |
87.6 |
| Croatian |
87.6 |
| Gothic |
16.5 |
| Swiss German |
48.3 |
| Assyrian |
14.6 |
| North Sami |
36.5 |
| Naija |
36.0 |
| Latvian |
86.6 |
| Chinese |
47.9 |
| Tagalog |
73.9 |
| Bambara |
29.7 |
| Lithuanian |
85.7 |
| Galician |
87.4 |
| Vietnamese |
65.1 |
| Greek |
86.3 |
| Catalan |
89.5 |
| Czech |
87.3 |
| Erzya |
43.0 |
| Bhojpuri |
48.5 |
| Thai |
58.1 |
| Marathi |
87.7 |
| Basque |
78.2 |
| Slovak |
88.2 |
| Kiche |
28.2 |
| Yoruba |
19.5 |
| Warlpiri |
27.9 |
| Tamil |
84.3 |
| Maltese |
19.2 |
| Ancient Greek |
66.3 |
| Icelandic |
84.3 |
| Mbya Guarani |
25.6 |
| Urdu |
68.5 |
| Romanian |
83.8 |
| Persian |
78.3 |
| Apurina |
27.3 |
| Japanese |
34.1 |
| Hungarian |
87.2 |
| Hindi |
73.3 |
| Classical Chinese |
28.3 |
| Komi Permyak |
45.1 |
| Faroese |
78.3 |
| Sanskrit |
30.3 |
| Livvi |
63.1 |
| Arabic |
80.0 |
| Wolof |
27.7 |
| Bulgarian |
89.2 |
| Akuntsu |
28.0 |
| Makurap |
7.5 |
| Kangri |
44.9 |
| Breton |
65.8 |
| Telugu |
85.7 |
| Cantonese |
50.7 |
| Old Church Slavonic |
49.4 |
| Karelian |
73.5 |
| Upper Sorbian |
70.9 |
| South Levantine Arabic |
64.8 |
| Komi Zyrian |
37.1 |
| Irish |
68.9 |
| Nayini |
46.2 |
| Munduruku |
12.3 |
| Manx |
35.7 |
| Skolt Sami |
30.1 |
| Afrikaans |
88.4 |
| Old Turkish |
37.1 |
| Tupinamba |
24.9 |
| Belarusian |
87.2 |
| Serbian |
89.0 |
| Moksha |
41.5 |
| Western Armenian |
79.0 |
| Scottish Gaelic |
59.5 |
| Khunsari |
40.5 |
| Hebrew |
94.8 |
| Uyghur |
77.2 |
| Chukchi |
30.5 |
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
PyTorch