Instructions to use msharma95/hml-bi-3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-bi-3000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-bi-3000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-bi-3000") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-bi-3000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 068f3986c462fcbd2e5a0ca86ebe4f90547a376e8c0f769e879c8fe5d9cc0ed5
- Size of remote file:
- 68.3 MB
- SHA256:
- aa1c03db8b2d70aab40fd590eee35302b035b8affa235dbd0efefead40ec6dfc
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