Instructions to use msharma95/hml-es-1700 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-es-1700 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-es-1700")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-es-1700") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-es-1700", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 191ba276aa19de0259b3bb3635fb8a4d2f6b7986da9bb9c56df0a584387076cf
- Size of remote file:
- 68.3 MB
- SHA256:
- 18a370966d6a02442560bdc34cb3dab392a2dfecdca56a24dbcfd26b411e65b2
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