metadata

language:
  - 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
  - kbd
  - 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, kbd_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:
  - nllb
  - translation
license: cc-by-nc-4.0
datasets:
  - flores-200
metrics:
  - bleu
  - spbleu
  - chrf++
inference: false
base_model:
  - facebook/nllb-200-1.3B

NLLB-200 1.3B Pre-trained for Kabardian Translation

Model Details

Model Name: nllb-200-1.3b-kbd-pretrain
Base Model: NLLB-200 1.3B
Model Type: Translation
Language(s): Kabardian and others from NLLB-200 (200 languages)
License: CC-BY-NC (inherited from base model)
Developer: panagoa (fine-tuning), Meta AI (base model)
Last Updated: January 23, 2025
Paper: NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022

Model Description

This model is a pre-trained adaptation of the NLLB-200 (No Language Left Behind) 1.3B parameter model that has been specifically optimized to improve translation capabilities for the Kabardian language (kbd). The base NLLB-200 model was developed by Meta AI and supports 200 languages, with this variant specifically adjusted for Kabardian language translation tasks.

Intended Uses

Machine translation to and from Kabardian
NLP applications involving the Kabardian language
Research on low-resource language translation
Cultural and linguistic preservation efforts for the Kabardian language

Training Data

This model has been pre-trained building upon the original NLLB-200 model, which used parallel multilingual data from various sources and monolingual data constructed from Common Crawl. The specific additional pre-training for Kabardian likely involved specialized Kabardian language resources.

The original NLLB-200 model was evaluated using the Flores-200 dataset.

Performance and Limitations

As a pre-trained model, this version is intended to be further fine-tuned for specific translation tasks
Inherits limitations from the base NLLB-200 model:
- Not intended for production deployment (research model)
- Not optimized for domain-specific texts (medical, legal, etc.)
- Not designed for document translation (optimized for single sentences)
- Training limited to input sequences not exceeding 512 tokens
- Translations cannot be used as certified translations
May have additional limitations when handling specific cultural contexts, dialectal variations, or specialized terminology in Kabardian

Usage Example

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "panagoa/nllb-200-1.3b-kbd-pretrain"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example: Translating to Kabardian
src_lang = "eng_Latn"  # English
tgt_lang = "kbd_Cyrl"  # Kabardian in Cyrillic script

text = "Hello, how are you?"
inputs = tokenizer(f"{src_lang}: {text}", return_tensors="pt")
translated_tokens = model.generate(
    **inputs, 
    forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang],
    max_length=30
)
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
print(translation)

Ethical Considerations

As noted for the base NLLB-200 model:

This work prioritizes human users and aims to minimize risks transferred to them
Translation access for low-resource languages can improve education and information access but could potentially make groups with lower digital literacy vulnerable to misinformation
Despite extensive data cleaning, personally identifiable information may not be entirely eliminated from training data
Mistranslations could have adverse impacts on those relying on translations for important decisions

Caveats and Recommendations

The base model was primarily tested on the Wikimedia domain with limited investigation on other domains
Supported languages may have variations that the model does not capture
Users should make appropriate assessments for their specific use cases
This pre-trained model is part of a series of models specifically focused on Kabardian language translation
For production use cases, consider the fully fine-tuned versions (v0.1, v0.2) rather than this pre-trained version

Additional Information

This model is part of a collection of NLLB models fine-tuned for Kabardian language translation developed by panagoa.