Instructions to use ainewtrend07/Normal-MiniLML6V2-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ainewtrend07/Normal-MiniLML6V2-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ainewtrend07/Normal-MiniLML6V2-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ainewtrend07/Normal-MiniLML6V2-classifier") model = AutoModelForSequenceClassification.from_pretrained("ainewtrend07/Normal-MiniLML6V2-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4874e0a331b5ccd86c26c58e56eaf4c5e859fbc5332ac72a18143ff063ebbe8b
- Size of remote file:
- 90.9 MB
- SHA256:
- e3f85068c90966fbce0f11683a1b79ee6010c4fada92b4ca3659371ad6a9aad5
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