Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use JuraKhil/feedback-analysis-rubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuraKhil/feedback-analysis-rubert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JuraKhil/feedback-analysis-rubert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JuraKhil/feedback-analysis-rubert") model = AutoModelForSequenceClassification.from_pretrained("JuraKhil/feedback-analysis-rubert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.6570411920547485
f1_macro: 0.30127311112532784
f1_micro: 0.6024096385542169
f1_weighted: 0.5182611323035088
precision_macro: 0.3755183891738514
precision_micro: 0.6024096385542169
precision_weighted: 0.5415404935249017
recall_macro: 0.33010204081632655
recall_micro: 0.6024096385542169
recall_weighted: 0.6024096385542169
accuracy: 0.6024096385542169
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Model tree for JuraKhil/feedback-analysis-rubert
Base model
cointegrated/rubert-tiny2