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
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |