Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:5749
loss:MatryoshkaLoss
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickprock/ModernBERT-large-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nickprock/ModernBERT-large-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nickprock/ModernBERT-large-sts") sentences = [ "The man talked to a girl over the internet camera.", "A group of elderly people pose around a dining table.", "A teenager talks to a girl over a webcam.", "There is no 'still' that is not relative to some other object." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from nickprock/ModernBERT-large-sts: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/nickprock/ModernBERT-large-sts/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://nickprock/ModernBERT-large-sts/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/nickprock/ModernBERT-large-sts/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "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 | |
| } |