Sentence Similarity
sentence-transformers
Safetensors
Turkish
bert
feature-extraction
Generated from Trainer
dataset_size:482091
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use x1saint/intfloat-triplet-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use x1saint/intfloat-triplet-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("x1saint/intfloat-triplet-v2") sentences = [ "Ya da dışarı çıkıp yürü ya da biraz koşun. Bunu düzenli olarak yapmıyorum ama Washington bunu yapmak için harika bir yer.", "“Washington's yürüyüş ya da koşu için harika bir yer.”", "H-2A uzaylılar Amerika Birleşik Devletleri'nde zaman kısa süreleri var.", "“Washington'da düzenli olarak yürüyüşe ya da koşuya çıkıyorum.”" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from x1saint/intfloat-triplet-v2: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/x1saint/intfloat-triplet-v2/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://x1saint/intfloat-triplet-v2/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/x1saint/intfloat-triplet-v2/resolve/main/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "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 | |
| } |