Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use mtyrrell/CPU_Conditional_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtyrrell/CPU_Conditional_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtyrrell/CPU_Conditional_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtyrrell/CPU_Conditional_Classifier") model = AutoModelForSequenceClassification.from_pretrained("mtyrrell/CPU_Conditional_Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c9e2e9c9e3eb5a4469814fe72d68b06b024daa6d638c6407c36ad4da5fa9bbd
- Size of remote file:
- 438 MB
- SHA256:
- 43b878cd8adea7ab12ffbcb42d96e16c759d8aee37f374e9776dd0b655f101d5
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