Model Details
Full Model IDnlptown/bert-base-multilingual-uncased-sentiment
Pipeline / Tasktext-classification
Librarytransformers
Downloads (all-time)1.0M
Likes474
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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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.
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⬇ Downloads1.0M
❤️ Community Likes474
🛠️ 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.