Text Classification
Transformers
Safetensors
xlm-roberta
emotion-classification
emotion
multi-label-classification
synthetic data
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
multilingual
🇪🇺
region:eu
Synthetic
text-embeddings-inference
Instructions to use tabularisai/multilingual-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tabularisai/multilingual-emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tabularisai/multilingual-emotion-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tabularisai/multilingual-emotion-classification") model = AutoModelForSequenceClassification.from_pretrained("tabularisai/multilingual-emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Initial upload: XLM-R-base multilingual multi-label emotion classifier (23 langs, 11 labels)
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