Instructions to use nlptown/bert-base-multilingual-uncased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlptown/bert-base-multilingual-uncased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1a774825ba30bf28a01c0ed6f555068d276ed9c0fc19d4e8e1a2e8b72170b16
- Size of remote file:
- 669 MB
- SHA256:
- 7988c7c3610880f76a734e6f7d46a8a8456ad459ac4910e9fd43b0bd3dc03583
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