Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use cduncanja/emotion_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cduncanja/emotion_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cduncanja/emotion_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cduncanja/emotion_model") model = AutoModelForSequenceClassification.from_pretrained("cduncanja/emotion_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62c5298b0a3faa4211d7a57e4b7f50194ad3de5dacd0014ba7ec6faebb89d372
- Size of remote file:
- 134 MB
- SHA256:
- ffdfb4bd3e4b1aa690ee9f1c9ac44a540402ec0e280c1ccb49808ee56845a311
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