Instructions to use l3cube-pune/oriya-question-answering-squad-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/oriya-question-answering-squad-bert with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="l3cube-pune/oriya-question-answering-squad-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/oriya-question-answering-squad-bert") model = AutoModelForQuestionAnswering.from_pretrained("l3cube-pune/oriya-question-answering-squad-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from l3cube-pune/oriya-question-answering-squad-bert: direct link, hf CLI and curl.
- Browser
- Download file 2.69 kB
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https://huggingface.co/l3cube-pune/oriya-question-answering-squad-bert/resolve/main/README.md
- Command line
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hf download hf://l3cube-pune/oriya-question-answering-squad-bert/README.md
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curl -L -o README.md https://huggingface.co/l3cube-pune/oriya-question-answering-squad-bert/resolve/main/README.md
2.69 kB
metadata
datasets:
- l3cube-pune/indic-squad
language: or
license: cc-by-4.0
library_name: transformers
pipeline_tag: question-answering
Oriya-BERT-SQuAD
Oriya-BERT-SQuAD is a OdiaBERT model (l3cube-pune/odia-bert) fine-tuned on the translated Odia question-answering dataset L3Cube-IndicSQuAD. [dataset link] (https://github.com/l3cube-pune/indic-nlp/tree/main/L3Cube-IndicSQUAD)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2505.03688)
Citing:
@article{endait2025indicsquad,
title={IndicSQuAD: A Comprehensive Multilingual Question Answering Dataset for Indic Languages},
author={Endait, Sharvi and Ghatage, Ruturaj and Kulkarni, Aditya and Patil, Rajlaxmi and Joshi, Raviraj},
journal={arXiv preprint arXiv:2505.03688},
year={2025}
}
@article{ghatage2024mahasquad,
title={MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering},
author={Ghatage, Ruturaj and Kulkarni, Aditya and Patil, Rajlaxmi and Endait, Sharvi and Joshi, Raviraj},
journal={arXiv preprint arXiv:2404.13364},
year={2024}
}
Other IndicSQuAD models:
| Language | Model Link |
|---|---|
| Marathi | marathi-question-answering-squad-bert |
| Hindi | hindi-question-answering-squad-bert |
| Bengali | bengali-question-answering-squad-bert |
| Telugu | telugu-question-answering-squad-bert |
| Tamil | tamil-question-answering-squad-bert |
| Gujarati | gujarati-question-answering-squad-bert |
| Punjabi | punjabi-question-answering-squad-bert |
| Kannada | kannada-question-answering-squad-bert |
| Oriya | oriya-question-answering-squad-bert |
| Malayalam | malayalam-question-answering-squad-bert |