Instructions to use monsoon-nlp/hindi-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monsoon-nlp/hindi-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="monsoon-nlp/hindi-bert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/hindi-bert") model = AutoModel.from_pretrained("monsoon-nlp/hindi-bert") - Notebooks
- Google Colab
- Kaggle
recommend larger models
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by georeactor - opened
README.md
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This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra).
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<a href="https://colab.research.google.com/drive/1R8TciRSM7BONJRBc9CBZbzOmz39FTLl_">Tokenization and training CoLab</a>
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This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra).
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**As of 2022 I recommend Google's MuRIL model trained on English, Hindi, and other major Indian languages, both in their script and latinized script**: https://huggingface.co/google/muril-base-cased and https://huggingface.co/google/muril-large-cased
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**For causal language models, I would suggest SberBank / mGPT, though this is a large model**
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<a href="https://colab.research.google.com/drive/1R8TciRSM7BONJRBc9CBZbzOmz39FTLl_">Tokenization and training CoLab</a>
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