# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-topic-medium-doc")
model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/marathi-topic-medium-doc")Quick Links
MahaNews-LPC-BERT
MahaNews-LPC-BERT is a MahaBERT(l3cube-pune/marathi-bert-v2) model fine-tuned on full L3Cube-MahaNews-LPC Corpus, a Marathi medium document / paragraph classification dataset.
It is a topic identification cum medium paragraph classification model with 12 output categories
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (coming soon)
Citing:
@inproceedings{mittal2023l3cube,
title={L3Cube-MahaNews: News-Based Short Text and Long Document Classification Datasets in Marathi},
author={Mittal, Saloni and Magdum, Vidula and Hiwarkhedkar, Sharayu and Dhekane, Omkar and Joshi, Raviraj},
booktitle={International Conference on Speech and Language Technologies for Low-resource Languages},
pages={52--63},
year={2023},
organization={Springer}
}
Other Marathi Sentiment models from MahaNews family are shared here:
MahaNews-LDC-BERT (long documents)
MahaNews-SHC-BERT (short text)
MahaNews-LPC-BERT (medium paragraphs)
MahaNews-All-BERT (all document lengths)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/marathi-topic-medium-doc")