Instructions to use machinelearningzuu/stress_sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use machinelearningzuu/stress_sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="machinelearningzuu/stress_sentiment_analysis", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("machinelearningzuu/stress_sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("machinelearningzuu/stress_sentiment_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93cd95b1dd950d386156fb0465105a488562025496932f158380691655cb9900
- Size of remote file:
- 499 MB
- SHA256:
- ab9960c54e4408558e81dfbf1e1d2b8cd081da6dafa1aefa1fe4320ff4fef8da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.