Instructions to use l3cube-pune/hindi-marathi-dev-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-marathi-dev-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hindi-marathi-dev-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-marathi-dev-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-marathi-dev-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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DevBERT is a Devanagari BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi and Marathi monolingual datasets.
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[project link] (https://github.com/l3cube-pune/MarathiNLP)
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More details on the dataset, models, and baseline results can be found in our [paper] (<a href='http://dx.doi.org/10.13140/RG.2.2.14606.84809'>
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```
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@article{joshi2022l3cubehind,
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DevBERT is a Devanagari BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi and Marathi monolingual datasets.
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[project link] (https://github.com/l3cube-pune/MarathiNLP)
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More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] (<a href='http://dx.doi.org/10.13140/RG.2.2.14606.84809'> pdf </a>)
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```
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@article{joshi2022l3cubehind,
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