Instructions to use Apoksk1/sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apoksk1/sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apoksk1/sentiment_analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apoksk1/sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("Apoksk1/sentiment_analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d532eefc61a7e1f452b42f305a31eef9de06241963509395b945670cf062c93
- Size of remote file:
- 5.18 kB
- SHA256:
- 34c20790112b2fc2935528e0804046e4100345953769afce5844410f3d3ece8a
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