Instructions to use vicd/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vicd/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vicd/sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vicd/sentiment") model = AutoModelForSequenceClassification.from_pretrained("vicd/sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cd283cecebcd7c5cc155b041ade3e632f5ed7408b3ef039ad75d7e23ac8cac4
- Size of remote file:
- 499 MB
- SHA256:
- 8d326fb7e71416fd3b734c8810e03eecb461c78ead78149151165bc545be7b53
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