Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9712
loss:TripletLoss
text-embeddings-inference
Instructions to use Syldehayem/all-MiniLM-L6-v2_embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Syldehayem/all-MiniLM-L6-v2_embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Syldehayem/all-MiniLM-L6-v2_embedder") sentences = [ "CGI VFX Breakdowns \"El Principe Season 1\" - by Stargate Studios Malta", "Best of 2013!", "কাজকর্ম ফেলে ছেলে নিয়ে পড়ে থাকলে হবে | Baro Bou | #shorts | #banglacinema", "CG animation on social anxiety | \"Subconcious Password\" - by Chris Landreth (Oscar-winner)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Syldehayem/all-MiniLM-L6-v2_embedder: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/Syldehayem/all-MiniLM-L6-v2_embedder/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Syldehayem/all-MiniLM-L6-v2_embedder/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Syldehayem/all-MiniLM-L6-v2_embedder/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 | |
| } |