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:
- b10d7c2b8a4efd2a0a5f904d2701d648111b77a47a7037cf530b03c6da5d85ec
- Size of remote file:
- 2.24 GB
- SHA256:
- fca6be153fcaf46489d1f967a543b08d19b16dbcd93255f37a9def5eaba2a13b
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