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 | { | |
| "embedding_dimension": 768, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |