Instructions to use DuboiJ/Sentiment_analysis_PI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DuboiJ/Sentiment_analysis_PI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DuboiJ/Sentiment_analysis_PI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DuboiJ/Sentiment_analysis_PI") model = AutoModelForSequenceClassification.from_pretrained("DuboiJ/Sentiment_analysis_PI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08fb2e2f440b45684d6aa17f45d3994fcf81995a9f3819e6185d4d73dd45e79f
- Size of remote file:
- 499 MB
- SHA256:
- 5b0ad1f71dc46fda4a6749f51e775e430acb06d861411abbdc060f2d4827124d
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