Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:44114
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use laura2243/bert-sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use laura2243/bert-sota with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("laura2243/bert-sota") sentences = [ "The city is located in 1889 , along the Nehalem River and Nehalem Bay , near the Pacific Ocean .", "Incorporated in 1889 , the city lies along the Pacific Ocean near the Nehalem River and Nehalem Bay .", "Along the coast there are almost 2,000 islands , about three quarters of which are uninhabited .", "The mammalian fauna of Madagascar is largely endemic and highly distinctive ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 612 Bytes
4d05b80 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"dtype": "float32",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.57.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
|