Instructions to use hapandya/xlmr-large-hi-te-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hapandya/xlmr-large-hi-te-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hapandya/xlmr-large-hi-te-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hapandya/xlmr-large-hi-te-MLM") model = AutoModelForMaskedLM.from_pretrained("hapandya/xlmr-large-hi-te-MLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78d80b74429748577678fdba12b90c028c61c1d8429956dab8661d07945b88f0
- Size of remote file:
- 2.74 kB
- SHA256:
- 75ad9e76d50f081c1c2d4b8e1d66ea3bc0675885bf6e913dcc05866dfa89cfff
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