MahaPOS-BERT: Marathi POS Tagging Model

Model Description

MahaPOS-BERT is a fine-tuned version of l3cube-pune/marathi-bert-v2 for Part-of-Speech (POS) tagging in Marathi. It is trained on the L3Cube-MahaPOS dataset — one of the first large-scale, manually annotated POS tagging datasets for Marathi — comprising 32,354 sentences drawn from Marathi news text.

This model is part of the L3Cube-MahaNLP project. For more details refer our MahaPOS paper.

Model Details

Label Set

The model uses a 16-tag set aligned with the Universal Dependencies (UD) v2 framework:

Note on POSTP: The POSTP tag has critically low corpus frequency (23 tokens total; 3 in the test set). It is retained in training but excluded from the primary macro-F1 evaluation metric. The X (foreign/unclassifiable) tag was removed entirely from training.

Performance

Per-Tag F1 Scores (Test Set)

Training Details

Subword-to-token alignment uses the first subword strategy: only the first subword token of each word is used for prediction; continuations receive the ignore label (-100).

Dataset: L3Cube-MahaPOS

The dataset was manually annotated by a team of Marathi-proficient annotators from PICT, Pune. Raw text was sourced from Marathi news portals covering politics, sports, culture, technology, and local affairs.

Usage

from transformers import pipeline

pipe = pipeline(
    "token-classification",
    model="l3cube-pune/marathi-pos-bert",  # update with actual HuggingFace repo path
    aggregation_strategy="first"
)

text = "भारत हा एक सुंदर देश आहे."
result = pipe(text)
for token in result:
    print(f"{token['word']:<15} {token['entity']}")

Loading Manually

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_path = "path/to/marathi_pos_final"  # local path or HF repo

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForTokenClassification.from_pretrained(model_path)

id2label = model.config.id2label

text = "नागपूर येथे मोठा कार्यक्रम झाला."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

predictions = outputs.logits.argmax(dim=-1)[0]
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])

for token, pred in zip(tokens, predictions):
    if token not in ["[CLS]", "[SEP]", "[PAD]"]:
        print(f"{token:<20} {id2label[pred.item()]}")

Model Files

Limitations

• Trained exclusively on formal news text; performance may degrade on informal, social media, or code-mixed Marathi.
• PROPN (F1: 59.52%) and INTJ (F1: 44.50%) are the weakest classes due to lack of capitalisation in Marathi and data sparsity respectively.
• POSTP (3 test tokens) cannot be reliably classified; treat its predictions as unreliable.
• Requires a GPU with ≥12 GB VRAM for fine-tuning.

Citation

If you use this model or dataset, please cite:

@article{ingle2026l3cube,
  title={L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models},
  author={Ingle, Hariom and Ghode, Ronit and Gondkar, Ishwari and Harad, Jidnyasa and Joshi, Raviraj},
  journal={arXiv preprint arXiv:2606.24825},
  year={2026}
}

Acknowledgements

This work was carried out under the mentorship of L3Cube Labs, Pune. This work is part of the L3Cube-MahaNLP project.