Text Classification
Transformers
PyTorch
Safetensors
English
custom_multilabel_emotion
emotion-classification
multilabel-classification
Instructions to use EnJiZ/FirstTimeUp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EnJiZ/FirstTimeUp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EnJiZ/FirstTimeUp")# Load model directly from transformers import MultiLabelEmotionClassifier model = MultiLabelEmotionClassifier.from_pretrained("EnJiZ/FirstTimeUp", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "MultiLabelEmotionClassifier" | |
| ], | |
| "model_type": "custom_multilabel_emotion", | |
| "base_model": "distilbert-base-uncased", | |
| "num_labels": 14, | |
| "emotion_labels": [ | |
| "amusement", | |
| "anger", | |
| "annoyance", | |
| "caring", | |
| "confusion", | |
| "disappointment", | |
| "disgust", | |
| "embarrassment", | |
| "excitement", | |
| "fear", | |
| "gratitude", | |
| "joy", | |
| "love", | |
| "sadness" | |
| ], | |
| "max_position_embeddings": 128, | |
| "dropout_rate": 0.3, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.21.0" | |
| } |