Instructions to use haval995/results_v2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haval995/results_v2.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="haval995/results_v2.2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("haval995/results_v2.2") model = AutoModelForSequenceClassification.from_pretrained("haval995/results_v2.2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11d7a20d9af128ddee7eba590318a913fc3c990b982fd072f5d486597c439085
- Size of remote file:
- 5.2 kB
- SHA256:
- 5927413d19a5c96a0c59aac0e0ed90faadf3dc7bfe9e3971a9ae7caf71fc5a3e
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