Text Classification
setfit
Safetensors
sentence-transformers
Danish
Few-Shot
Transformers
Text-classification
generated_from_setfit_traine
Computational_humanities
SSH
Social-work
Instructions to use CALDISS-AAU/da-reported-speech-e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use CALDISS-AAU/da-reported-speech-e5 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("CALDISS-AAU/da-reported-speech-e5") - sentence-transformers
How to use CALDISS-AAU/da-reported-speech-e5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CALDISS-AAU/da-reported-speech-e5") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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- **Developed by:** CALDISS, AAU
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- **Funded by
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- **Model type:**
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- **Language(s) (NLP):**
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- **License:**
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- **Finetuned from model
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### Model Sources
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- **Repository:** [Project repository](https://github.com/CALDISS-AAU/bp_SMI_CM)
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- **Developed by:** CALDISS, AAU
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- **Funded by:** Aalborg University
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- **Model type:** Few-Shot text-Classifier
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- **Language(s) (NLP):** Danish
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- **License:** MIT
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- **Finetuned from model intfloat/Multilingual-e5-large:**
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### Model Sources
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- **Repository:** [Project repository](https://github.com/CALDISS-AAU/bp_SMI_CM)
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