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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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example_title: "Negative - NMBS"
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- text: "De NMBS is een vervoermaatschappij."
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example_title: "Neutral - NMBS"
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---
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# RePublic
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example_title: "Negative - NMBS"
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- text: "De NMBS is een vervoermaatschappij."
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example_title: "Neutral - NMBS"
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- text: "Dat was een belachelijk goede film."
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example_title: "Positive - Movies"
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- text: "Het FWO financiert te weinig excellent onderzoek."
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example_title: "Negative - FWO"
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- text: "De wegenwerken langs de E34 blijven al maanden aanhouden."
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example_title: "Negative - AWV"
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- text: "De Lijn is op zoek naar nieuwe buschauffeurs."
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example_title: "Neutral - De Lijn"
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- text: "Natuur en Bos plant de heraanleg van bosgebied."
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example_title: "Neutral - ANB"
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---
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# RePublic
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