Instructions to use JohanHeinsen/PE_header_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JohanHeinsen/PE_header_classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JohanHeinsen/PE_header_classifier") 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] - setfit
How to use JohanHeinsen/PE_header_classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JohanHeinsen/PE_header_classifier") - Notebooks
- Google Colab
- Kaggle
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README.md
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- sentence-transformers
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- text-classification
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pipeline_tag: text-classification
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---
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#
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This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. It was created to identify headers in the publication Politiets Efterretninger (1867–1890)
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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- sentence-transformers
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- text-classification
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pipeline_tag: text-classification
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base_model:
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- CALDISS-AAU/DA-BERT_Old_News_V1
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- JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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---
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# PE_header_classifier
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This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. It was created to identify headers in the publication Politiets Efterretninger (1867–1890)
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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