Instructions to use JJ-Tae/Pretraining_MFM_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JJ-Tae/Pretraining_MFM_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JJ-Tae/Pretraining_MFM_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JJ-Tae/Pretraining_MFM_v3") model = AutoModelForMaskedLM.from_pretrained("JJ-Tae/Pretraining_MFM_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7c9734e051e56c5fc02f354c6cc9de12a892e38cdb40a7dbf9a5b6c6429d257
- Size of remote file:
- 557 MB
- SHA256:
- 96470c27bd86c0eff5406b7d188cf8b8113b27b3d577f171c7c0097de68fee83
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