Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use JuraKhil/feedbackAnalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuraKhil/feedbackAnalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JuraKhil/feedbackAnalysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JuraKhil/feedbackAnalysis") model = AutoModelForSequenceClassification.from_pretrained("JuraKhil/feedbackAnalysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.5224248170852661
f1_macro: 0.2334369154360309
f1_micro: 0.5421686746987951
f1_weighted: 0.44577861120813117
precision_macro: 0.2296899529042386
precision_micro: 0.5421686746987951
precision_weighted: 0.4092523941921532
recall_macro: 0.2891156462585034
recall_micro: 0.5421686746987951
recall_weighted: 0.5421686746987951
accuracy: 0.5421686746987951
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Model tree for JuraKhil/feedbackAnalysis
Base model
google-bert/bert-base-uncased