Text Classification
Transformers
TensorBoard
Safetensors
camembert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Tenta42/pneumatix-label-2024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tenta42/pneumatix-label-2024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tenta42/pneumatix-label-2024")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tenta42/pneumatix-label-2024") model = AutoModelForSequenceClassification.from_pretrained("Tenta42/pneumatix-label-2024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.4111526608467102
f1_macro: 0.5810333438459235
f1_micro: 0.883818380115952
f1_weighted: 0.881267831489644
precision_macro: 0.6062763684058178
precision_micro: 0.883818380115952
precision_weighted: 0.8805285475681943
recall_macro: 0.5663281551006615
recall_micro: 0.883818380115952
recall_weighted: 0.883818380115952
accuracy: 0.883818380115952
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Model tree for Tenta42/pneumatix-label-2024
Base model
almanach/camembert-large