Instructions to use S2312dal/M1_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M1_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="S2312dal/M1_MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("S2312dal/M1_MLM") model = AutoModelForMaskedLM.from_pretrained("S2312dal/M1_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from S2312dal/M1_MLM: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/S2312dal/M1_MLM/resolve/main/training_args.bin
- Command line
-
hf download hf://S2312dal/M1_MLM/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/S2312dal/M1_MLM/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 2d7c0abe011de47b675da8dc568a7990e682c6af2a4042688935e6662a6dc1dd
- Size of remote file:
- 3.18 kB
- SHA256:
- 429521363358ac63d6107b19407079f340cc7b6640eebffd1d20ef6fc3e23aa1
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