Text Classification
Transformers
PyTorch
Dutch
bert
text classification
sentiment analysis
domain adaptation
text-embeddings-inference
Instructions to use clips/republic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clips/republic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clips/republic")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clips/republic") model = AutoModelForSequenceClassification.from_pretrained("clips/republic") - Notebooks
- Google Colab
- Kaggle
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# RePublic
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### Model description
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RePublic (<u>re</u>putation analyzer for <u>public</u> service organizations) is a Dutch BERT model based on BERTje (De Vries, 2019). The model was designed to predict the sentiment in Dutch-language news article text about public agencies. RePublic was developed by CLiPS in collaboration with [Jan Boon](https://www.uhasselt.be/nl/wie-is-wie/jan-boon).
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### How to use
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The model can be loaded and used to make predictions as follows:
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# RePublic
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### Model description
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RePublic (<u>re</u>putation analyzer for <u>public</u> service organizations) is a Dutch BERT model based on BERTje (De Vries, 2019). The model was designed to predict the sentiment in Dutch-language news article text about public agencies. RePublic was developed by [CLiPS](https://www.uantwerpen.be/en/research-groups/clips/) in collaboration with Prof. Dr. [Jan Boon](https://www.uhasselt.be/nl/wie-is-wie/jan-boon).
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### How to use
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The model can be loaded and used to make predictions as follows:
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