Text Classification
Transformers
TensorBoard
ONNX
Safetensors
distilbert
Trained with AutoTrain
text-embeddings-inference
Instructions to use aimanfadillah/params-categories-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aimanfadillah/params-categories-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aimanfadillah/params-categories-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aimanfadillah/params-categories-v2") model = AutoModelForSequenceClassification.from_pretrained("aimanfadillah/params-categories-v2") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.2110723853111267
f1_macro: 0.902925457054823
f1_micro: 0.9640613167966272
f1_weighted: 0.9638800956459441
precision_macro: 0.9160039299507144
precision_micro: 0.9640613167966272
precision_weighted: 0.9642234403441109
recall_macro: 0.8989848262567356
recall_micro: 0.9640613167966272
recall_weighted: 0.9640613167966272
accuracy: 0.9640613167966272
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Model tree for aimanfadillah/params-categories-v2
Base model
distilbert/distilbert-base-uncased