Instructions to use serbog/distilbert-jobCategory_370k_BCEWithLogitsLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use serbog/distilbert-jobCategory_370k_BCEWithLogitsLoss with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="serbog/distilbert-jobCategory_370k_BCEWithLogitsLoss")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("serbog/distilbert-jobCategory_370k_BCEWithLogitsLoss") model = AutoModelForSequenceClassification.from_pretrained("serbog/distilbert-jobCategory_370k_BCEWithLogitsLoss", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8a3c60727b90746e2afaa40d34ccad4b6b0a3fa29d8b77d1b70123787e1f52a
- Size of remote file:
- 4.09 kB
- SHA256:
- b4e24e64947b3c113dae1720453a8ba1a81ccd5d0d7d1b3307fe23b7aa4a5db7
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