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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Removed www from url in readme.md, there is no www in the dns (#10)
Browse files- Removed www from url, there is no www in the dns (07141cea8ddd69cb71b96dd460e7ad4a9303f596)
Co-authored-by: De Ee <machprod@users.noreply.huggingface.co>
README.md
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## Contact
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In addition to this model, [NLP Town](https://
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Feel free to contact us for questions, feedback and/or requests for similar models.
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## Contact
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In addition to this model, [NLP Town](https://nlp.town) offers custom, monolingual sentiment models for many languages and an improved multilingual model through [RapidAPI](https://rapidapi.com/nlp-town-nlp-town-default/api/multilingual-sentiment-analysis2/).
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Feel free to contact us for questions, feedback and/or requests for similar models.
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