Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dataset_size:n<1K
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use nicolassaint/mpnet-base-all-nli-triplet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nicolassaint/mpnet-base-all-nli-triplet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nicolassaint/mpnet-base-all-nli-triplet") sentences = [ "Double pig.", "Ah, triple pig!", "On the square outline.", "a boy sleeps in a car" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |