Instructions to use Harry214/paleography-web-text-triage-logreg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Harry214/paleography-web-text-triage-logreg with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Harry214/paleography-web-text-triage-logreg") 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
Create README.md
Browse files
README.md
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---
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language:
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- zh
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license: mit
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library_name: scikit-learn
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tags:
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- text-classification
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- sentence-transformers
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- embeddings
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- paleography
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---
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# Paleography Web Text Triage Classifier
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This repository contains the final Logistic Regression classifier for the Paleography Web Text Triage project.
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The model classifies short Chinese paleography-related snippets into four labels:
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| Label | Meaning |
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|---|---|
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| `ksd` | Scholarly discussion |
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| `kpt` | Primary transcription |
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| `kde` | Dictionary/reference entry |
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| `noise` | Noise or irrelevant text |
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## Method
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The system uses `intfloat/multilingual-e5-small` to encode each text snippet with the prefix:
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```text
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passage: {text}
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