Instructions to use HJOK/task2_deberta_spamMLM_v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HJOK/task2_deberta_spamMLM_v6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="HJOK/task2_deberta_spamMLM_v6", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HJOK/task2_deberta_spamMLM_v6") model = AutoModel.from_pretrained("HJOK/task2_deberta_spamMLM_v6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9d702511a63154ad867b5ce1e22e5fac27046734163f5b74041284857f07c0d
- Size of remote file:
- 388 MB
- SHA256:
- 84bc926cf5bbd61f591383dd5352666de9ab49fa10b5b731d2ab5447728e6e62
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