Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:10189
loss:DistillationTripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Data-Lab/USER-bge-m3-embedder_distill-tg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Data-Lab/USER-bge-m3-embedder_distill-tg with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Data-Lab/USER-bge-m3-embedder_distill-tg") sentences = [ "фисташки", "Батончик ореховый \"Арахис-фисташка\" батончик, орехи, арахис, фисташка, тыквенные семена, фруктоза, пребиотик, мобильный перекус, десерт, натуральные ингредиенты, лёгкий перекус", "Торт \"Белые ночи\" с сырным кремом торт, десерт, сырный крем, шоколад, глазурь, бисквит, праздничный, угощение, аллергены, домашняя выпечка", "Фисташки жареные, 1 кг None, фисташки, жареные, сладкие, закуска, орехи, скорлупа, 1 кг" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Data-Lab/USER-bge-m3-embedder_distill-tg: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/Data-Lab/USER-bge-m3-embedder_distill-tg/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Data-Lab/USER-bge-m3-embedder_distill-tg/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Data-Lab/USER-bge-m3-embedder_distill-tg/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "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 | |
| } |