Text Classification
Transformers
PyTorch
English
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use nihaldsouza1/yelp-rating-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nihaldsouza1/yelp-rating-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nihaldsouza1/yelp-rating-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nihaldsouza1/yelp-rating-classification") model = AutoModelForSequenceClassification.from_pretrained("nihaldsouza1/yelp-rating-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48b9a4800e923ac3c87635b71cc70dc070720602988fd7112572131847a03cea
- Size of remote file:
- 329 MB
- SHA256:
- 21b59bd68cb84d6f8961481767e6858d16a7e9634016e4a6cd94d92f89e7218a
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