Sentence Similarity
sentence-transformers
Safetensors
English
French
German
xlm-roberta
feature-extraction
job-titles
retrieval
contrastive-learning
matryoshka
cross-lingual
esco
onet
text-embeddings-inference
Instructions to use Misbahuddin/job-title-normalizer-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Misbahuddin/job-title-normalizer-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Misbahuddin/job-title-normalizer-e5-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Point demo link at Cloud Run deployment
Browse files
README.md
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@@ -27,7 +27,7 @@ occupation taxonomy** (ESCO + O*NET), framed and evaluated as **retrieval**: emb
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foreign-language title (the *query*) and retrieve the closest canonical occupation label (the
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*passage*) from a fixed corpus of 4,055 occupations.
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**Live demo:**
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**Lighter variant:** [job-title-normalizer-e5-small](https://huggingface.co/Misbahuddin/job-title-normalizer-e5-small)
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> **Why retrieval, not classification?** Occupation taxonomies have thousands of classes and the
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foreign-language title (the *query*) and retrieve the closest canonical occupation label (the
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*passage*) from a fixed corpus of 4,055 occupations.
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**Live demo:** <https://job-title-normalizer-525186107937.us-central1.run.app> (Cloud Run; may cold-start ~1 min) 路
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**Lighter variant:** [job-title-normalizer-e5-small](https://huggingface.co/Misbahuddin/job-title-normalizer-e5-small)
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> **Why retrieval, not classification?** Occupation taxonomies have thousands of classes and the
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