McGill-NLP/AfroXLMR-large-76L-Injongo-slot

INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages

Evaluation Comparison

Intent Detection (Accuracy)

In-language training

Model	eng	amh	ewe	hau	ibo	kin	lin	lug	orm	sna	sot	swa	twi	wol	xho	yor	zul	AVG
mT5-Large	80.5	91.5	77.3	94.6	92.9	83.7	91.3	83.3	73.3	92.6	80.2	95.8	85.3	91.6	95.8	90.9	82.4	87.7±4.1
AfriTeVa V2 (T5)	81.6	93.2	84.4	98.9	95.7	87.8	91.6	86.8	86.6	94.6	85.7	96.8	87.1	94.0	97.3	97.0	89.2	91.7±2.7
NLLB LLM2Vec	88.4	94.2	87.8	98.3	96.8	89.2	95.2	93.2	86.2	96.1	87.3	97.4	93.5	95.6	97.5	97.3	89.1	93.4±2.3
XLM-RoBERTa	83.5	92.9	77.9	96.0	88.8	69.6	90.5	78.9	75.0	83.8	76.0	96.7	79.5	90.2	89.6	92.6	74.7	84.5±4.9
AfriBERTa V2	74.2	91.2	78.3	98.2	93.8	83.1	91.0	83.8	78.8	89.5	81.9	96.0	83.2	92.3	94.4	95.0	86.7	88.6±3.5
AfroXLMR	84.1	95.3	84.6	98.3	96.0	88.2	93.3	85.2	88.3	95.3	85.5	97.8	88.8	95.8	97.3	96.1	89.0	92.2±3.0
AfroXLMR 76L	84.5	95.5	90.4	98.7	96.3	89.4	94.6	91.3	88.3	95.1	86.8	98.1	93.6	96.2	96.9	97.7	89.8	93.7±2.1

Multi-lingual training

Model	eng	amh	ewe	hau	ibo	kin	lin	lug	orm	sna	sot	swa	twi	wol	xho	yor	zul	AVG
AfroXLMR-large-76L-Injongo-intent	89.0	96.0	92.6	99.2	96.6	87.7	95.9	92.3	92.9	96.5	87.6	97.8	94.2	97.1	97.3	97.9	89.2	94.4±2.0

Slot Filling (F1)

In-language training

Model	eng	amh	ewe	hau	ibo	kin	lin	lug	orm	sna	sot	swa	twi	wol	xho	yor	zul	AVG
mT5-Large	73.7	80.9	71.6	89.4	80.5	74.2	82.6	78.9	72.1	81.1	74.7	88.1	79.0	76.9	88.4	78.9	68.3	79.1±3.7
AfriTeVa V2 (T5)	73.6	80.9	74.5	93.8	79.9	76.6	87.1	85.2	79.0	82.1	77.5	88.9	84.0	79.0	90.0	87.2	71.2	82.3±3.3
NLLB LLM2Vec	74.6	82.4	80.5	93.6	78.1	70.1	84.8	86.6	80.8	81.4	74.8	85.7	85.7	78.3	88.0	85.0	78.3	82.1±3.1
XLM-RoBERTa	77.9	84.8	79.9	93.9	76.6	69.3	86.3	83.8	83.8	79.3	71.7	88.7	84.2	79.3	89.1	83.9	79.4	82.1±3.5
AfriBERTa V2	70.7	82.2	77.9	93.7	78.3	73.8	84.4	84.1	81.0	81.8	73.5	87.6	81.9	78.3	88.5	86.2	79.6	82.1±2.9
AfroXLMR	79.0	86.2	81.6	95.1	82.0	76.3	87.1	88.5	84.9	84.9	77.5	90.2	85.5	81.7	91.1	87.3	82.5	85.2±2.7
AfroXLMR 76L	78.7	86.3	84.5	94.3	81.9	76.7	88.0	88.8	85.5	84.9	77.4	90.2	89.8	81.3	90.5	88.1	81.3	85.6±2.7

Multi-lingual training

Model	eng	amh	ewe	hau	ibo	kin	lin	lug	orm	sna	sot	swa	twi	wol	xho	yor	zul	AVG
AfroXLMR-large-76L-Injongo-slot	82.4	88.2	87.0	96.3	84.0	79.3	90.3	89.2	87.2	86.1	80.4	90.5	90.3	83.3	91.8	90.2	83.3	87.3±2.4

Language Codes

• eng: English
• amh: Amharic
• ewe: Ewe
• hau: Hausa
• ibo: Igbo
• kin: Kinyarwanda
• lin: Lingala
• lug: Luganda
• orm: Oromo
• sna: Shona
• sot: Sesotho
• swa: Swahili
• twi: Twi
• wol: Wolof
• xho: Xhosa
• yor: Yoruba
• zul: Zulu

Notes

• Bold values indicate the best performing scores in each category
• The highlighted models (AfroXLMR 76L) show the top overall performance
• Multi-lingual training generally outperforms in-language training
• Standard deviations are reported alongside average scores
• AVG doest not include english results.

Citation

@misc{yu2025injongo,
      title={INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages}, 
      author={Hao Yu and Jesujoba O. Alabi and Andiswa Bukula and Jian Yun Zhuang and En-Shiun Annie Lee and Tadesse Kebede Guge and Israel Abebe Azime and Happy Buzaaba and Blessing Kudzaishe Sibanda and Godson K. Kalipe and Jonathan Mukiibi and Salomon Kabongo Kabenamualu and Mmasibidi Setaka and Lolwethu Ndolela and Nkiruka Odu and Rooweither Mabuya and Shamsuddeen Hassan Muhammad and Salomey Osei and Sokhar Samb and Juliet W. Murage and Dietrich Klakow and David Ifeoluwa Adelani},
      year={2025},
      eprint={2502.09814},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.09814}, 
}

@misc{adelani2023sib200,
      title={SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects}, 
      author={David Ifeoluwa Adelani and Hannah Liu and Xiaoyu Shen and Nikita Vassilyev and Jesujoba O. Alabi and Yanke Mao and Haonan Gao and Annie En-Shiun Lee},
      year={2023},
      eprint={2309.07445},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}