Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Tenta42/topiX-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tenta42/topiX-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tenta42/topiX-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tenta42/topiX-v2") model = AutoModelForSequenceClassification.from_pretrained("Tenta42/topiX-v2") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.39918211102485657
f1_macro: 0.41505954741496226
f1_micro: 0.8790214217715892
f1_weighted: 0.8712094509362144
precision_macro: 0.45123624523232514
precision_micro: 0.8790214217715892
precision_weighted: 0.8685143627382157
recall_macro: 0.41461617856662464
recall_micro: 0.8790214217715892
recall_weighted: 0.8790214217715892
accuracy: 0.8790214217715892
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Model tree for Tenta42/topiX-v2
Base model
sileod/deberta-v3-base-tasksource-nli