Instructions to use tdobrxl/Sentiment_analysis_PI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdobrxl/Sentiment_analysis_PI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tdobrxl/Sentiment_analysis_PI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tdobrxl/Sentiment_analysis_PI") model = AutoModelForSequenceClassification.from_pretrained("tdobrxl/Sentiment_analysis_PI") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6712d3cc118e28b1fa3cace6dcee913e9027516b5db127d0691337f636b7dbb
- Size of remote file:
- 499 MB
- SHA256:
- 746e75c05353d4c76f60fe334aac9305bb75964fe73464fde3ad06ca003a258e
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