Instructions to use hapandya/mBERT-hi-te-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hapandya/mBERT-hi-te-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hapandya/mBERT-hi-te-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hapandya/mBERT-hi-te-MLM") model = AutoModelForMaskedLM.from_pretrained("hapandya/mBERT-hi-te-MLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a5cb3b2365f7d48582686c96c0e03e9f7dcfd902f1be9aebd17c133e55d1d2b
- Size of remote file:
- 712 MB
- SHA256:
- 494b601640ace048147f7a0e550bc06e095ba95e3c5d198e31508496e7051421
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