Instructions to use JohanHeinsen/ENO_header_identifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JohanHeinsen/ENO_header_identifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JohanHeinsen/ENO_header_identifier") 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/ENO_header_identifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JohanHeinsen/ENO_header_identifier") - 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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This is a setfit-model designed to classify text lines as headers or non-headers. It is designed to help segmentation of ENO.
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## Metrics:
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Accuracy: 0.9912186763760976
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f1: 0.9715869715869716
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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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- JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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---
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This is a setfit-model designed to classify text lines as headers or non-headers. It is designed to help segmentation of ENO.
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## Metrics:
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Accuracy: 0.9912186763760976
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f1: 0.9715869715869716
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