Text Classification
Transformers
Safetensors
distilbert
Trained with AutoTrain
text-embeddings-inference
Instructions to use borgg-takes-all/alpha-prompt-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use borgg-takes-all/alpha-prompt-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="borgg-takes-all/alpha-prompt-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("borgg-takes-all/alpha-prompt-classification") model = AutoModelForSequenceClassification.from_pretrained("borgg-takes-all/alpha-prompt-classification") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.03381425514817238
f1_macro: 0.9910410929202866
f1_micro: 0.9908675799086758
f1_weighted: 0.9908473335613555
precision_macro: 0.9909727371947719
precision_micro: 0.9908675799086758
precision_weighted: 0.9908883151237302
recall_macro: 0.9911698494022667
recall_micro: 0.9908675799086758
recall_weighted: 0.9908675799086758
accuracy: 0.9908675799086758
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