Instructions to use msharma95/hml-en-300 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-en-300 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-en-300")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-en-300") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-en-300", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9fc2e6e44f0bd947e68f0f29773372f0b0bc0081e503fa5e29e2af3bdab8507f
- Size of remote file:
- 68.3 MB
- SHA256:
- d6d317774fb188cd2c24fb71a4340bad60012130ec356a64966780f50367d9e8
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