Instructions to use ishathombre/monolingual-hindi-from-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ishathombre/monolingual-hindi-from-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ishathombre/monolingual-hindi-from-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ishathombre/monolingual-hindi-from-scratch") model = AutoModelForMaskedLM.from_pretrained("ishathombre/monolingual-hindi-from-scratch") - Notebooks
- Google Colab
- Kaggle
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README.md
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# BERT from Scratch (1 Epoch, Training Loss: 4.13)
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- **Training:** 1 epoch
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- **Masked Language Modeling (MLM) loss:** 4.13
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# BERT from Scratch (1 Epoch, Training Loss: 4.13)
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BERT model trained from scratch using a custom tokenizer with a 64,000-token vocabulary.
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- **Training:** 1 epoch
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- **Masked Language Modeling (MLM) loss:** 4.13
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