Instructions to use tqfang229/deberta-v3-large-com2-atomic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tqfang229/deberta-v3-large-com2-atomic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tqfang229/deberta-v3-large-com2-atomic")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tqfang229/deberta-v3-large-com2-atomic") model = AutoModelForMaskedLM.from_pretrained("tqfang229/deberta-v3-large-com2-atomic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12434d7bbb28ea39c188354f528ae0cb01fac3a47969025bc3a09a0adbede450
- Size of remote file:
- 1.74 GB
- SHA256:
- 5f9e50c31777d2402062072d7ea15663f5a6b50395c09328957f06df9b7f7138
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