Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:9738
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use vkimbris/bge-m3-item-matcher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vkimbris/bge-m3-item-matcher with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vkimbris/bge-m3-item-matcher") sentences = [ "Молоко 3,2% 'БМК' (здж уклон) БЗМЖ 1 л*12", "EMPTY", "Кокосовое молоко Esoro", "EMPTY" ] 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 | |
| } |