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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bf2e36aa1696f7381b5593962e8f5466933121790b4c263a8fe68e1faa50aa1
- Size of remote file:
- 438 MB
- SHA256:
- 43075e99a587dda39c3392387f0cf65954850f064c1231034954b2f8c30b8a20
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