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
File size: 538 Bytes
2fa8178 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"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"
} |