Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use Barzillian/knowledge-Distillation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Barzillian/knowledge-Distillation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Barzillian/knowledge-Distillation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Barzillian/knowledge-Distillation") model = AutoModelForSequenceClassification.from_pretrained("Barzillian/knowledge-Distillation") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.646373450756073
f1_macro: 0.7298437618326189
f1_micro: 0.7447447447447447
f1_weighted: 0.7467396946425192
precision_macro: 0.7348636077090247
precision_micro: 0.7447447447447447
precision_weighted: 0.7644157119297172
recall_macro: 0.7382166799546654
recall_micro: 0.7447447447447447
recall_weighted: 0.7447447447447447
accuracy: 0.7447447447447447
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Model tree for Barzillian/knowledge-Distillation
Base model
FacebookAI/roberta-base