Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use scales-okn/ontology-motion-to-certify-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scales-okn/ontology-motion-to-certify-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scales-okn/ontology-motion-to-certify-class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scales-okn/ontology-motion-to-certify-class") model = AutoModelForSequenceClassification.from_pretrained("scales-okn/ontology-motion-to-certify-class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88150ada5385c108fb9bf1320a82e10f09b29819ac5c9e0af29f7a3166f40e7e
- Size of remote file:
- 1.74 GB
- SHA256:
- 5caeaa2107cf06062eccdaf96a29f16fd22d75c28f90a53fd3e81a3ca9d5b315
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