Instructions to use savasy/TurkQP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use savasy/TurkQP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="savasy/TurkQP")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("savasy/TurkQP") model = AutoModelForSequenceClassification.from_pretrained("savasy/TurkQP") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42951f4bcce52c76c3c169ce27b080660c84459b9bb9b4a18ff48cc65dc0b2f8
- Size of remote file:
- 442 MB
- SHA256:
- 47a7b5c12f278c0f48d00ef379eb0fc927ba47fad07e49d58177ff8da26ba635
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