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 modules.json from Syldehayem/all-MiniLM-L6-v2_embedder: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/Syldehayem/all-MiniLM-L6-v2_embedder/resolve/main/modules.json
- Command line
-
hf download hf://Syldehayem/all-MiniLM-L6-v2_embedder/modules.json
-
curl -L -o modules.json https://huggingface.co/Syldehayem/all-MiniLM-L6-v2_embedder/resolve/main/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |