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xmod-base

X-MOD is a multilingual masked language model trained on filtered CommonCrawl data containing 81 languages. It was introduced in the paper Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., NAACL 2022) and first released in this repository.

Because it has been pre-trained with language-specific modular components (language adapters), X-MOD differs from previous multilingual models like XLM-R. For fine-tuning, the language adapters in each transformer layer are frozen.

Usage

Tokenizer

This model reuses the tokenizer of XLM-R.

Input Language

Because this model uses language adapters, you need to specify the language of your input so that the correct adapter can be activated:

from transformers import XmodModel

model = XmodModel.from_pretrained("facebook/xmod-base")
model.set_default_language("en_XX")

A directory of the language adapters in this model is found at the bottom of this model card.

Fine-tuning

In the experiments in the original paper, the embedding layer and the language adapters are frozen during fine-tuning. A method for doing this is provided in the code:

model.freeze_embeddings_and_language_adapters()
# Fine-tune the model ...

Cross-lingual Transfer

After fine-tuning, zero-shot cross-lingual transfer can be tested by activating the language adapter of the target language:

model.set_default_language("de_DE")
# Evaluate the model on German examples ...

Bias, Risks, and Limitations

Please refer to the model card of XLM-R, because X-MOD has a similar architecture and has been trained on similar training data.

Citation

BibTeX:

@inproceedings{pfeiffer-etal-2022-lifting,
    title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
    author = "Pfeiffer, Jonas  and
      Goyal, Naman  and
      Lin, Xi  and
      Li, Xian  and
      Cross, James  and
      Riedel, Sebastian  and
      Artetxe, Mikel",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.255",
    doi = "10.18653/v1/2022.naacl-main.255",
    pages = "3479--3495"
}

Languages

This model contains the following language adapters:

lang_id (Adapter index) Language code Language
0 en_XX English
1 id_ID Indonesian
2 vi_VN Vietnamese
3 ru_RU Russian
4 fa_IR Persian
5 sv_SE Swedish
6 ja_XX Japanese
7 fr_XX French
8 de_DE German
9 ro_RO Romanian
10 ko_KR Korean
11 hu_HU Hungarian
12 es_XX Spanish
13 fi_FI Finnish
14 uk_UA Ukrainian
15 da_DK Danish
16 pt_XX Portuguese
17 no_XX Norwegian
18 th_TH Thai
19 pl_PL Polish
20 bg_BG Bulgarian
21 nl_XX Dutch
22 zh_CN Chinese (simplified)
23 he_IL Hebrew
24 el_GR Greek
25 it_IT Italian
26 sk_SK Slovak
27 hr_HR Croatian
28 tr_TR Turkish
29 ar_AR Arabic
30 cs_CZ Czech
31 lt_LT Lithuanian
32 hi_IN Hindi
33 zh_TW Chinese (traditional)
34 ca_ES Catalan
35 ms_MY Malay
36 sl_SI Slovenian
37 lv_LV Latvian
38 ta_IN Tamil
39 bn_IN Bengali
40 et_EE Estonian
41 az_AZ Azerbaijani
42 sq_AL Albanian
43 sr_RS Serbian
44 kk_KZ Kazakh
45 ka_GE Georgian
46 tl_XX Tagalog
47 ur_PK Urdu
48 is_IS Icelandic
49 hy_AM Armenian
50 ml_IN Malayalam
51 mk_MK Macedonian
52 be_BY Belarusian
53 la_VA Latin
54 te_IN Telugu
55 eu_ES Basque
56 gl_ES Galician
57 mn_MN Mongolian
58 kn_IN Kannada
59 ne_NP Nepali
60 sw_KE Swahili
61 si_LK Sinhala
62 mr_IN Marathi
63 af_ZA Afrikaans
64 gu_IN Gujarati
65 cy_GB Welsh
66 eo_EO Esperanto
67 km_KH Central Khmer
68 ky_KG Kirghiz
69 uz_UZ Uzbek
70 ps_AF Pashto
71 pa_IN Punjabi
72 ga_IE Irish
73 ha_NG Hausa
74 am_ET Amharic
75 lo_LA Lao
76 ku_TR Kurdish
77 so_SO Somali
78 my_MM Burmese
79 or_IN Oriya
80 sa_IN Sanskrit
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