Instructions to use dnzblgn/Sentiment-Analysis-Customer-Reviews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dnzblgn/Sentiment-Analysis-Customer-Reviews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dnzblgn/Sentiment-Analysis-Customer-Reviews")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews") model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d9daef676ffa8b0f774f874fc4f3206580795162f6a222a754667a2e0d10516
- Size of remote file:
- 16.4 MB
- SHA256:
- be21b6f938c490bffd7a7bfac7a3322bf5173937b2186bb13c7adb747176e52a
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