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 | [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize" | |
| } | |
| ] |