swahili-pos-tagger-afroxlmr

masakhane/swahili-pos-tagger-afroxlmr

Model description

swahili-pos-tagger-afroxlmr is a POS tagger for Swahili language based on MasakhaPOS dataset.

Intended uses & limitations

How to use

You can use this model with Transformers pipeline for POS.

from transformers import AutoTokenizer, AutoModelForTokenClassification, TokenClassificationPipeline

model_name = "masakhane/swahili-pos-tagger-afroxlmr"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
outputs = pipeline("Saa 1 iliyopitaMoto unaosambaa kwa kasi wasababisha maelfu ya watu kuhamishwa huko California")
print(outputs)

Limitations and bias

This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.

Training data

This model was fine-tuned on Swahili POS dataset with the UD POS tags

BibTeX entry and citation info

@inproceedings{dione-etal-2023-masakhapos,
    title = "{M}asakha{POS}: Part-of-Speech Tagging for Typologically Diverse {A}frican languages",
    author = "Dione, Cheikh M. Bamba  and
      Adelani, David Ifeoluwa  and
      Nabende, Peter  and
      Alabi, Jesujoba  and
      Sindane, Thapelo  and
      Buzaaba, Happy  and
      Muhammad, Shamsuddeen Hassan  and
      Emezue, Chris Chinenye  and
      Ogayo, Perez  and
      Aremu, Anuoluwapo  and
      Gitau, Catherine  and
      Mbaye, Derguene  and
      Mukiibi, Jonathan  and
      Sibanda, Blessing  and
      Dossou, Bonaventure F. P.  and
      Bukula, Andiswa  and
      Mabuya, Rooweither  and
      Tapo, Allahsera Auguste  and
      Munkoh-Buabeng, Edwin  and
      Memdjokam Koagne, Victoire  and
      Ouoba Kabore, Fatoumata  and
      Taylor, Amelia  and
      Kalipe, Godson  and
      Macucwa, Tebogo  and
      Marivate, Vukosi  and
      Gwadabe, Tajuddeen  and
      Elvis, Mboning Tchiaze  and
      Onyenwe, Ikechukwu  and
      Atindogbe, Gratien  and
      Adelani, Tolulope  and
      Akinade, Idris  and
      Samuel, Olanrewaju  and
      Nahimana, Marien  and
      Musabeyezu, Th{\'e}og{\`e}ne  and
      Niyomutabazi, Emile  and
      Chimhenga, Ester  and
      Gotosa, Kudzai  and
      Mizha, Patrick  and
      Agbolo, Apelete  and
      Traore, Seydou  and
      Uchechukwu, Chinedu  and
      Yusuf, Aliyu  and
      Abdullahi, Muhammad  and
      Klakow, Dietrich",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.609/",
    doi = "10.18653/v1/2023.acl-long.609",
    pages = "10883--10900",
    abstract = "In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages."
}