Instructions to use NimaKL/MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NimaKL/MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NimaKL/MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NimaKL/MLM") model = AutoModelForMaskedLM.from_pretrained("NimaKL/MLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a401d30ff26db9809436c047a0e34e53d42c8974f9679dce53a1119e9144e2e
- Size of remote file:
- 443 MB
- SHA256:
- 2f3c679200875d16aeef597cde50b376fc8e3c312f67b5ab9fc683363a55ffa0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.