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:
- c9e6d62cab23be7695d12e0f5e79c20daca49c18869dc86c86241ce7a0a60d26
- Size of remote file:
- 1.12 GB
- SHA256:
- d8b5edf86a7d4279993ecdeafc1aa803457bea306b11df36f09c22dc229eae58
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