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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tabularisai/multilingual-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("tabularisai/multilingual-sentiment-analysis") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ed902103439752e65e6561b907ef736cea77d778091d4253ac0ccae9cad2ea5
- Size of remote file:
- 541 MB
- SHA256:
- 3ab3cecb8605da0a240e5b4e18d969704d44e27c6ea48533ef6693d31dbb926a
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