Instructions to use Seethal/sentimentanalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Seethal/sentimentanalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Seethal/sentimentanalysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Seethal/sentimentanalysis") model = AutoModelForSequenceClassification.from_pretrained("Seethal/sentimentanalysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d052f380aa0267ab0c3f86021f589b31c9da5ceede0ff538cafd2ef4836c2a4b
- Size of remote file:
- 2.99 kB
- SHA256:
- 4b8e057605b86b91da42c7fb1355e16308bdac150e7a4e855b225370df6d48c4
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