Text Classification
Transformers
Safetensors
distilbert
sentiment-analysis
sentiment
synthetic data
multi-class
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
multilingual
🇪🇺
region:eu
Synthetic
text-embeddings-inference
Instructions to use tabularisai/multilingual-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tabularisai/multilingual-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tabularisai/multilingual-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("tabularisai/multilingual-sentiment-analysis", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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Synthetic data reduces bias, but validation in real-world scenarios is advised.
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## Citation
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```
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## Contact
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Synthetic data reduces bias, but validation in real-world scenarios is advised.
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## Citation
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```bib
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@misc{tabularisai_2025,
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author = { tabularisai and Samuel Gyamfi and Vadim Borisov and Richard H. Schreiber },
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title = { multilingual-sentiment-analysis (Revision 69afb83) },
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year = 2025,
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url = { https://huggingface.co/tabularisai/multilingual-sentiment-analysis },
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doi = { 10.57967/hf/5968 },
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publisher = { Hugging Face }
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}
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```
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## Contact
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