🏷️ Text Classification

bert-base-multilingual-uncased-sentiment

nlptown/bert-base-multilingual-uncased-sentiment

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transformers
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Model Details
Full Model IDnlptown/bert-base-multilingual-uncased-sentiment
Pipeline / Tasktext-classification
Librarytransformers
Downloads (all-time)958.6K
Likes479
Last Modified1/2/2025
Author / Orgnlptown
PrivateNo — public
⚡ Quick Usage (Python)

Using the 🤗 Transformers library. Install with pip install transformers

from transformers import pipeline

# Load the model
pipe = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")

# Run inference
result = pipe("Your input here")
print(result)
🏷️ Tags
transformerspytorchtfjaxsafetensorsberttext-classificationennldefritesdoi:10.57967/hf/1515license:mitendpoints_compatibledeploy:azureregion:us
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🚀 Use This Model

Access model files, inference API, and full documentation on Hugging Face.

Open on Hugging Face →Browse Model Files ↗← Browse All Models
🏷️ Task: Text Classification

This model is designed for the Text Classification task. Explore more models for this use case.

All Text Classification Models →
📊 Popularity
Downloads958.6K
❤️ Community Likes479
🛠️ Requirements
  • Install: pip install transformers
  • Python 3.8+ recommended for Transformers.
  • GPU (CUDA) speeds up inference significantly.
  • Use model.half() for fp16 on limited VRAM.
👋 Need help with code?