Instructions to use dnzblgn/Customer-Reviews-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dnzblgn/Customer-Reviews-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dnzblgn/Customer-Reviews-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Customer-Reviews-Classification") model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Customer-Reviews-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79da748e586d337354e4cadffc920f1e2c85223f50fae46300f6b73b198750eb
- Size of remote file:
- 268 MB
- SHA256:
- b858fa34c5daacff7c8eb5fcdf906891c5cf43436d216431f52266b12ebba6c1
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