Instructions to use rahular/varta-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahular/varta-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rahular/varta-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rahular/varta-bert") model = AutoModelForMaskedLM.from_pretrained("rahular/varta-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Varta-BERT is a model pre-trained on the `full` training set of [Varta](https://huggingface.co/datasets/rahular/varta) in 14 Indic languages (Assamese, Bhojpuri, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, and Urdu) and English, using a masked language modeling (MLM) objective.
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[Varta](https://huggingface.co/datasets/rahular/varta) is a large-scale news corpus for Indic languages, including 41.8 million news articles in 14 different Indic languages (and English), which come from a variety of high-quality sources.
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The dataset and the model are introduced in [this paper](https://arxiv.org/abs/2305.05858). The code is released in [this repository](https://github.com/rahular/varta).
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## Uses
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You can use the raw model for masked language modeling, but it is mostly intended to be fine-tuned on a downstream task.
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Varta-BERT is a model pre-trained on the `full` training set of [Varta](https://huggingface.co/datasets/rahular/varta) in 14 Indic languages (Assamese, Bhojpuri, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, and Urdu) and English, using a masked language modeling (MLM) objective.
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[Varta](https://huggingface.co/datasets/rahular/varta) is a large-scale news corpus for Indic languages, including 41.8 million news articles in 14 different Indic languages (and English), which come from a variety of high-quality sources.
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The dataset and the model are introduced in [this paper](https://arxiv.org/abs/2305.05858). The code is released in [this repository](https://github.com/rahular/varta).
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## Uses
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You can use the raw model for masked language modeling, but it is mostly intended to be fine-tuned on a downstream task.
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