COMI-LINGUA-POS

About Model

Hindi-English POS Tagger

A LoRA-adapted Transformer LLM fine-tuned for token-level Part-of-Speech (POS) tagging on Hindi–English (Hinglish) code-mixed text.

Supported POS Tags

• NOUN
• PROPN
• VERB
• ADJ
• ADV
• ADP
• PRON
• DET
• CONJ
• PART
• PRON_WH
• PART_NEG
• NUM
• X (typos, punctuation, abbreviations, foreign elements)

Model Overview

• Model type: LoRA-adapted Transformer LLM
• Base model: CohereForAI/aya-expanse-8b
• Total parameters: 8B
• Trainable parameters: ~32M
• License: Apache 2.0

Performance

Achieves 88.61 F1 on the COMI-LINGUA POS test set (5K instances), competitive with or slightly outperforming specialized traditional tools and surpassing strong zero-/one-shot LLM baselines.

Example Inference

Assign Part-of-Speech (POS) tags to each token in the sentence given as:

मीराबाई चानू ने 21 st Commonwealth Games में India के लिए first Gold medal जीता था।

Output:

[
  {"मीराबाई": "PROPN"},
  {"चानू": "PROPN"},
  {"ने": "PART"},
  {"21": "NUM"},
  {"st": "X"},
  {"Commonwealth": "PROPN"},
  {"Games": "PROPN"},
  {"में": "ADP"},
  {"India": "PROPN"},
  {"के": "ADP"},
  {"लिए": "ADP"},
  {"first": "ADJ"},
  {"Gold": "NOUN"},
  {"medal": "NOUN"},
  {"जीता": "VERB"},
  {"था": "VERB"},
  {"।": "X"}
]

Citation

@inproceedings{sheth-etal-2025-comi,
  title     = "{COMI}-{LINGUA}: Expert Annotated Large-Scale Dataset for Multitask {NLP} in {H}indi-{E}nglish Code-Mixing",
  author    = "Sheth, Rajvee and
               Beniwal, Himanshu and
               Singh, Mayank",
  editor    = "Christodoulopoulos, Christos and
               Chakraborty, Tanmoy and
               Rose, Carolyn and
               Peng, Violet",
  booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
  month     = nov,
  year      = "2025",
  address   = "Suzhou, China",
  publisher = "Association for Computational Linguistics",
  url       = "https://aclanthology.org/2025.findings-emnlp.422/",
  pages     = "7973--7992",
  isbn      = "979-8-89176-335-7"
}