Instructions to use shed-e/MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="shed-e/MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shed-e/MLM") model = AutoModelForMaskedLM.from_pretrained("shed-e/MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shed-e/MLM: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/shed-e/MLM/resolve/main/training_args.bin
- Command line
-
hf download hf://shed-e/MLM/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shed-e/MLM/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 98fcc90e19ccc57b006e708557bdd6ed76950ae9f1b434d05cacf4b4b34ebcd1
- Size of remote file:
- 3.38 kB
- SHA256:
- 5d9e945e21b17eafab599a9ee72fef1b3f2a48cd85f3577ede7eb6b6cb93c8f8
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