Text Classification
Transformers
Safetensors
English
Russian
xlm-roberta
emotion-classification
multi-label-classification
goemotions
english
russian
affective-computing
text-embeddings-inference
Instructions to use proxy3d/multi-motions-28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use proxy3d/multi-motions-28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="proxy3d/multi-motions-28")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("proxy3d/multi-motions-28") model = AutoModelForSequenceClassification.from_pretrained("proxy3d/multi-motions-28", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 936 Bytes
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"admiration": 0.5,
"amusement": 0.30000001192092896,
"anger": 0.5,
"annoyance": 0.1599999964237213,
"approval": 0.20000000298023224,
"caring": 0.3199999928474426,
"confusion": 0.18000000715255737,
"curiosity": 0.23999999463558197,
"desire": 0.41999998688697815,
"disappointment": 0.17000000178813934,
"disapproval": 0.14000000059604645,
"disgust": 0.3400000035762787,
"embarrassment": 0.38999998569488525,
"excitement": 0.3799999952316284,
"fear": 0.12999999523162842,
"gratitude": 0.6200000047683716,
"grief": 0.3799999952316284,
"joy": 0.33000001311302185,
"love": 0.3700000047683716,
"nervousness": 0.3199999928474426,
"optimism": 0.3799999952316284,
"pride": 0.4000000059604645,
"realization": 0.17000000178813934,
"relief": 0.05999999865889549,
"remorse": 0.20999999344348907,
"sadness": 0.4699999988079071,
"surprise": 0.33000001311302185,
"neutral": 0.20999999344348907
} |