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
File size: 494 Bytes
a679dc5 | 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 29 30 31 32 33 34 | {
"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
}
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