Sentence Similarity
sentence-transformers
Safetensors
Transformers
German
bert
feature-extraction
text-embeddings-inference
Instructions to use lwolfrum2/careerbert-jg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lwolfrum2/careerbert-jg with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lwolfrum2/careerbert-jg") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use lwolfrum2/careerbert-jg with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lwolfrum2/careerbert-jg") model = AutoModel.from_pretrained("lwolfrum2/careerbert-jg") - Inference
- Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |