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 pytorch_model.bin from shed-e/MLM: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/shed-e/MLM/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shed-e/MLM/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shed-e/MLM/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 01eb70a2ff8489163c16ed83712d22cd21490176aea7860434ed8d89ec8089f0
- Size of remote file:
- 268 MB
- SHA256:
- ee81037b51f7afbd91eb1f2a5ba1105fe4579ff0dfd3e1f850877259875b4c65
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