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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 626c1cccdc9f701017af225311f3682d4d76e6db27be6d6934694764d19cf778
- Size of remote file:
- 139 MB
- SHA256:
- 7d2b110473599ac5e3abee7b321868a3f0188a107d3ddd3ca4f79112df14e77c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.