Text Classification
Transformers
Safetensors
distilbert
Sentiment Analysis
Text Classification
BERT
Yelp Reviews
Fine-tuned
text-embeddings-inference
Instructions to use kmack/YELP-Review_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmack/YELP-Review_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kmack/YELP-Review_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kmack/YELP-Review_Classifier") model = AutoModelForSequenceClassification.from_pretrained("kmack/YELP-Review_Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08722897fbb5fc8ceb65c87e2ec3a444d5b248d0bc301af6c4cf9180e93e4c91
- Size of remote file:
- 268 MB
- SHA256:
- 71d5c22cc7d46409ea2612de3dee75b0659364d4b226da807247042dd58e618d
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