Sentence Similarity
sentence-transformers
Safetensors
English
French
German
bert
feature-extraction
job-titles
retrieval
contrastive-learning
matryoshka
cross-lingual
esco
onet
text-embeddings-inference
Instructions to use Misbahuddin/job-title-normalizer-e5-small 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-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Misbahuddin/job-title-normalizer-e5-small") 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-small job-title normalizer (ESCO+ONET, InfoNCE + Matryoshka)
c0b55f5 verified | { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 64, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |