Instructions to use msharma95/hml-en-500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-en-500 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-en-500")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-en-500") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-en-500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eea1b0ce37aaf990c72154eca85983cdd316df9f690877f58b66af7735b4a548
- Size of remote file:
- 68.3 MB
- SHA256:
- da7c63f787769e496fc86afe73d81857f9b314db491449f1bf77b564613d1294
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