Instructions to use Seethal/sentiment_analysis_generic_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Seethal/sentiment_analysis_generic_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Seethal/sentiment_analysis_generic_dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Seethal/sentiment_analysis_generic_dataset") model = AutoModelForSequenceClassification.from_pretrained("Seethal/sentiment_analysis_generic_dataset") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33d9cb848de95e3064111790bb808a3770e3fd92158f994716211ee1278c266e
- Size of remote file:
- 3.06 kB
- SHA256:
- fdaa01d41c9d14b1fb003185ed284a9bc82ea63f822745d9104cb838194dbbaa
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