Instructions to use HJOK/task2_deberta_spamMLM_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HJOK/task2_deberta_spamMLM_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="HJOK/task2_deberta_spamMLM_v2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HJOK/task2_deberta_spamMLM_v2") model = AutoModel.from_pretrained("HJOK/task2_deberta_spamMLM_v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b6e9322ece59d94b994eefde14a7ec719ccc8a641fe4a5761343ebcd3784291
- Size of remote file:
- 388 MB
- SHA256:
- e17a36599fa10ba9890113ce224ee04f507604a2a08954793a83e6409c608a1d
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