Instructions to use msharma95/hml-en-last with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-en-last with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-en-last")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-en-last") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-en-last", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fc4df211a96c29716d9c649c148d29d501d47a35fc8dd02c28c15d2847e710c
- Size of remote file:
- 68.3 MB
- SHA256:
- 01587443b43034a6c847a06a2c7ef530bcab5c3b38a892cf33339446855c0402
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