Instructions to use kanad13/emotion_detection_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanad13/emotion_detection_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kanad13/emotion_detection_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kanad13/emotion_detection_model") model = AutoModelForSequenceClassification.from_pretrained("kanad13/emotion_detection_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8f1157e4cbd33507c073f53fdbb16ca526fe495dc7e0634aa91df4a028281ed
- Size of remote file:
- 438 MB
- SHA256:
- ebbad817820bb5e11d0de06b06c41344dcee484903c510f9949aa783cf5f0b3b
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