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:
- 85094292a5a1c8c732fa96e5598aed75eb4e42862d80a20ace0d823ac0b683c2
- Size of remote file:
- 5.24 kB
- SHA256:
- 6a3c35d00b3a9f09f09bbbd95adfdc6deaf5070563e785852debaae072505dcb
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