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 MihaBEST/rubert-tiny2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MihaBEST/rubert-tiny2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MihaBEST/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 MihaBEST/rubert-tiny2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("MihaBEST/rubert-tiny2") model = AutoModelForPreTraining.from_pretrained("MihaBEST/rubert-tiny2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_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 | |
| } | |