Sentence Similarity
sentence-transformers
Safetensors
Russian
English
bert
embeddings
vllm
inference-optimized
inference
text-embeddings-inference
Instructions to use WpythonW/rubert-tiny2-vllm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use WpythonW/rubert-tiny2-vllm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("WpythonW/rubert-tiny2-vllm") sentences = [ "Это счастливый человек", "Это счастливая собака", "Это очень счастливый человек", "Сегодня солнечный день" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 684 Bytes
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"_name_or_path": "cointegrated/rubert-tiny2",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"emb_size": 312,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 312,
"initializer_range": 0.02,
"intermediate_size": 600,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 2048,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 3,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.12.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 83828
}
|