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") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
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Download README.md from JohanHeinsen/ENO_header_identifier: direct link, hf CLI and curl.
- Browser
- Download file 374 Bytes
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https://huggingface.co/JohanHeinsen/ENO_header_identifier/resolve/main/README.md
- Command line
-
hf download hf://JohanHeinsen/ENO_header_identifier/README.md
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curl -L -o README.md https://huggingface.co/JohanHeinsen/ENO_header_identifier/resolve/main/README.md
374 Bytes
metadata
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
base_model:
- JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
This is a setfit-model designed to classify text lines as headers or non-headers. It is designed to help segmentation of ENO.
Metrics:
Accuracy: 0.9912186763760976 f1: 0.9715869715869716