Text Classification
Transformers
TensorBoard
Safetensors
roberta
yelp
sentiment-analysis
polarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use itserphan/yelp-polarity-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use itserphan/yelp-polarity-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="itserphan/yelp-polarity-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("itserphan/yelp-polarity-roberta") model = AutoModelForSequenceClassification.from_pretrained("itserphan/yelp-polarity-roberta") - Notebooks
- Google Colab
- Kaggle
π Yelp Polarity Classifier (RoBERTa-base)
This model is a fine-tuned version of roberta-base on the Yelp Polarity dataset.
It classifies reviews as Positive β or Negative π with high accuracy.
π Quick Use
from transformers import pipeline
classifier = pipeline("text-classification", model="itserphan/yelp-polarity-roberta")
print(classifier("The burger was amazing, I'll definitely come back!"))
# [{'label': 'Positive', 'score': 0.998}]
print(classifier("Terrible service. Food was cold and overpriced."))
# [{'label': 'Negative', 'score': 0.996}]
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Model tree for itserphan/yelp-polarity-roberta
Base model
FacebookAI/roberta-baseSpace using itserphan/yelp-polarity-roberta 1
Evaluation results
- accuracy on Yelp Polarityself-reported0.962
- precision on Yelp Polarityself-reported0.961
- recall on Yelp Polarityself-reported0.964
- f1 on Yelp Polarityself-reported0.962