Instructions to use mgh6/TCS_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/TCS_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/TCS_MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/TCS_MLM") model = AutoModelForMaskedLM.from_pretrained("mgh6/TCS_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a663ab07bb6225e9019df70f32cb349c5cf8959df65d8ab968a3076b0c410a96
- Size of remote file:
- 268 MB
- SHA256:
- 5d7d8e2181fbd44c975aa71b4db7db47e5e346397a615f83767017f384dda543
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