Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Russian
bert
pretraining
russian
fill-mask
embeddings
masked-lm
tiny
feature-extraction
text-embeddings-inference
Instructions to use cointegrated/rubert-tiny2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cointegrated/rubert-tiny2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cointegrated/rubert-tiny2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use cointegrated/rubert-tiny2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2") model = AutoModelForPreTraining.from_pretrained("cointegrated/rubert-tiny2") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 693 Bytes
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"_name_or_path": "cointegrated/rubert-tiny2",
"architectures": [
"BertForPreTraining"
],
"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
}
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