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
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README.md
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## Training procedure
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The model hyperparameters were tuned using ```optuna``` over 10 trials on a truncated training and validation dataset. The model was then trained over 5 epochs using the best hyperparameters identified.
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## Training procedure
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The model hyperparameters were tuned using ```optuna``` over 10 trials on a truncated training and validation dataset. The model was then trained over 5 epochs using the best hyperparameters identified.
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