Sentence Similarity
sentence-transformers
Safetensors
English
bert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:9233417
loss:ArcFaceInBatchLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-experimental") sentences = [ "Hayley Vaughan portrayed Ripa on the ABC daytime soap opera , `` All My Children `` , between 1990 and 2002 .", "Traxxpad is a music application for Sony 's PlayStation Portable published by Definitive Studios and developed by Eidos Interactive .", "Between 1990 and 2002 , Hayley Vaughan Ripa portrayed in the ABC soap opera `` All My Children `` .", "Between 1990 and 2002 , Ripa Hayley portrayed Vaughan in the ABC soap opera `` All My Children `` ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 612 Bytes
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"architectures": [
"BertModel"
],
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"classifier_dropout": null,
"dtype": "bfloat16",
"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",
"transformers_version": "4.56.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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