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Misbahuddin
/
job-title-normalizer-e5-base

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
Model card Files Files and versions
xet
Community

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
job-title-normalizer-e5-base / eval
140 Bytes
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
Misbahuddin's picture
Misbahuddin
Fine-tuned multilingual-e5-base job-title normalizer (ESCO+ONET, InfoNCE + Matryoshka)
6e93963 verified 2 days ago
  • similarity_evaluation_val-synonym-vs-nonsynonym_results.csv
    140 Bytes
    Fine-tuned multilingual-e5-base job-title normalizer (ESCO+ONET, InfoNCE + Matryoshka) 2 days ago