Nllb 200 3.3B

🚀 NLLB-200

NLLB-200 is a machine translation model that enables single - sentence translation among 200 languages. It's mainly designed for machine - translation research, especially for low - resource languages.

📦 Supported Languages

The model supports the following languages:

ace, acm, acq, aeb, af, ajp, ak, als, am, apc, ar, ars, ary, arz, as, ast, awa, ayr, azb, azj, ba, bm, ban, be, bem, bn, bho, bjn, bo, bs, bug, bg, ca, ceb, cs, cjk, ckb, crh, cy, da, de, dik, dyu, dz, el, en, eo, et, eu, ee, fo, fj, fi, fon, fr, fur, fuv, gaz, gd, ga, gl, gn, gu, ht, ha, he, hi, hne, hr, hu, hy, ig, ilo, id, is, it, jv, ja, kab, kac, kam, kn, ks, ka, kk, kbp, kea, khk, km, ki, rw, ky, kmb, kmr, knc, kg, ko, lo, lij, li, ln, lt, lmo, ltg, lb, lua, lg, luo, lus, lvs, mag, mai, ml, mar, min, mk, mt, mni, mos, mi, my, nl, nn, nb, npi, nso, nus, ny, oc, ory, pag, pa, pap, pbt, pes, plt, pl, pt, prs, quy, ro, rn, ru, sg, sa, sat, scn, shn, si, sk, sl, sm, sn, sd, so, st, es, sc, sr, ss, su, sv, swh, szl, ta, taq, tt, te, tg, tl, th, ti, tpi, tn, ts, tk, tum, tr, tw, tzm, ug, uk, umb, ur, uzn, vec, vi, war, wo, xh, ydd, yo, yue, zh, zsm, zu

📋 Language Details

ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn

📄 Tags and Related Information

📚 Model Information

This is the model card of NLLB - 200's 3.3B variant. Here are the metrics for that particular checkpoint.

• Training Information: Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB - 200 is described in the paper.
• Reference: NLLB Team et al, No Language Left Behind: Scaling Human - Centered Machine Translation, Arxiv, 2022
• License: CC - BY - NC
• Feedback Channel: Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues

✨ Main Features

• Multilingual Translation: Enables single - sentence translation among 200 languages.
• Research - Oriented: Primarily designed for machine - translation research, especially for low - resource languages.

🚀 Quick Start

For information on how to use the model, you can find it in the Fairseq code repository along with the training code and references to evaluation and training data.

💻 Usage Scope

Intended Use

• Primary Use: NLLB - 200 is a machine translation model primarily intended for research in machine translation, especially for low - resource languages.
• Primary Users: The primary users are researchers and the machine translation research community.

Out - of - Scope Use

• NLLB - 200 is a research model and is not released for production deployment.
• It is trained on general domain text data and is not intended to be used with domain - specific texts, such as medical or legal domain texts.
• The model is not intended for document translation.
• Since the model was trained with input lengths not exceeding 512 tokens, translating longer sequences might result in quality degradation.
• NLLB - 200 translations cannot be used as certified translations.

📊 Metrics

• Model Performance Measures: The NLLB - 200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by the machine translation community. Additionally, human evaluation was performed with the XSTS protocol, and the toxicity of the generated translations was measured.

📈 Evaluation Data

• Datasets: The Flores - 200 dataset is described in Section 4.
• Motivation: Flores - 200 was used as it provides full evaluation coverage of the languages in NLLB - 200.
• Preprocessing: Sentence - split raw text data was preprocessed using SentencePiece. The SentencePiece model is released along with NLLB - 200.

📈 Training Data

• Parallel multilingual data from a variety of sources was used to train the model. A detailed report on data selection and construction process is provided in Section 5 in the paper.
• Monolingual data constructed from Common Crawl was also used. More details are provided in Section 5.2.

🔧 Ethical Considerations

In this work, a reflexive approach was taken in technological development to prioritize human users and minimize risks transferred to them. Here are some additional points:

• Many of the languages chosen for this study are low - resource languages, with a focus on African languages. While quality translation can improve education and information access in these communities, it can also make groups with lower digital literacy more vulnerable to misinformation or online scams.
• The training data for model development was mined from various publicly available web sources. Although significant efforts were made in data cleaning, personally identifiable information may not be completely eliminated.
• Despite efforts to optimize translation quality, the model may still produce mistranslations, which could have adverse impacts on those relying on these translations for important decisions (especially regarding health and safety).

⚠️ Caveats and Recommendations

• The model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB - MD.
• The supported languages may have variations that the model does not capture. Users should make appropriate assessments.

🌱 Carbon Footprint Details

The carbon dioxide (CO2e) estimate is reported in Section 8.8.