Instructions to use HJOK/task2_deberta_spamMLM_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HJOK/task2_deberta_spamMLM_v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="HJOK/task2_deberta_spamMLM_v5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HJOK/task2_deberta_spamMLM_v5") model = AutoModel.from_pretrained("HJOK/task2_deberta_spamMLM_v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28ffe31a981d285284bc73e95aa8d97818fb22e9a6683a8814cff8bffa939c95
- Size of remote file:
- 388 MB
- SHA256:
- 449527906163bf6c01d9f090232cc6b0f22b6de75a1747c0a5cd629129422e4d
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