Sentence Similarity
sentence-transformers
Safetensors
Hungarian
modernbert
feature-extraction
Generated from Trainer
dataset_size:1044013
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use karsar/ModernBERT-base-hu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use karsar/ModernBERT-base-hu with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("karsar/ModernBERT-base-hu") sentences = [ "Emberek várnak a lámpánál kerékpárral.", "Az emberek piros lámpánál haladnak.", "Az emberek a kerékpárjukon vannak.", "Egy fekete kutya úszik a vízben egy teniszlabdával a szájában" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from karsar/ModernBERT-base-hu: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/karsar/ModernBERT-base-hu/resolve/main/modules.json
- Command line
-
hf download hf://karsar/ModernBERT-base-hu/modules.json
-
curl -L -o modules.json https://huggingface.co/karsar/ModernBERT-base-hu/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |