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
Fine-tuned multilingual-e5-base job-title normalizer (ESCO+ONET, InfoNCE + Matryoshka)
6e93963 verified - Xet hash:
- cf1a7d46c8f90fc189231ff21302e1e550085bb86067b7c6d4912abec265fcd6
- Size of remote file:
- 17.1 MB
- SHA256:
- bc5c1151948923156f20bcafd54fd796705d693f8d7b56c83aec49d651f6d602
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