Instructions to use Blablablab/10dimensions-respect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Blablablab/10dimensions-respect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Blablablab/10dimensions-respect")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Blablablab/10dimensions-respect") model = AutoModelForSequenceClassification.from_pretrained("Blablablab/10dimensions-respect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ccfad61c4bf8746cc4278d9c7150b8b45b438fcdc3a8c98aec7123f2b77f7a7b
- Size of remote file:
- 433 MB
- SHA256:
- 402b82fc0a505b1c7b2a6eeef6ad41c5d5c6209b0919f9491aed36a98e4f7583
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