Text Classification
Transformers
Safetensors
English
roberta
goemotions
emotion-classification
multi-label-classification
roberta-large
focal-loss
threshold-optimization
nlp
Eval Results (legacy)
text-embeddings-inference
Instructions to use AliceYin/goemotions-roberta-large-focal-sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AliceYin/goemotions-roberta-large-focal-sota with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AliceYin/goemotions-roberta-large-focal-sota")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AliceYin/goemotions-roberta-large-focal-sota") model = AutoModelForSequenceClassification.from_pretrained("AliceYin/goemotions-roberta-large-focal-sota") - Notebooks
- Google Colab
- Kaggle
| { | |
| "label_names": [ | |
| "admiration", | |
| "amusement", | |
| "anger", | |
| "annoyance", | |
| "approval", | |
| "caring", | |
| "confusion", | |
| "curiosity", | |
| "desire", | |
| "disappointment", | |
| "disapproval", | |
| "disgust", | |
| "embarrassment", | |
| "excitement", | |
| "fear", | |
| "gratitude", | |
| "grief", | |
| "joy", | |
| "love", | |
| "nervousness", | |
| "optimism", | |
| "pride", | |
| "realization", | |
| "relief", | |
| "remorse", | |
| "sadness", | |
| "surprise", | |
| "neutral" | |
| ], | |
| "neutral_index": 27 | |
| } | |