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:
- 96e00f8f960352288c71ddb18294473d99550b03b825ae1453a5016a745d20f6
- Size of remote file:
- 268 MB
- SHA256:
- 7feb21fd3a5d763ba2216a542fd64cc27e37534dbaf8da25299326b97372b3ae
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