Instructions to use dejanseo/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dejanseo/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dejanseo/sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dejanseo/sentiment") model = AutoModelForSequenceClassification.from_pretrained("dejanseo/sentiment") - Notebooks
- Google Colab
- Kaggle
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Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).
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The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline.
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This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.
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Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).
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To see this model in action visit: [Sentiment Tool](https://dejanmarketing.com/tools/sentiment/)
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The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline.
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This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.
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