Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Uchindami/malawi-jobs-classification-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Uchindami/malawi-jobs-classification-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Uchindami/malawi-jobs-classification-deberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Uchindami/malawi-jobs-classification-deberta") model = AutoModelForSequenceClassification.from_pretrained("Uchindami/malawi-jobs-classification-deberta") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 2.0943500995635986
f1_macro: 0.04253472222222222
f1_micro: 0.21052631578947367
f1_weighted: 0.09886695906432748
precision_macro: 0.034640522875816995
precision_micro: 0.21052631578947367
precision_weighted: 0.0737530099759202
recall_macro: 0.08241758241758242
recall_micro: 0.21052631578947367
recall_weighted: 0.21052631578947367
accuracy: 0.21052631578947367
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Model tree for Uchindami/malawi-jobs-classification-deberta
Base model
microsoft/deberta-v3-base