metadata
license: unlicense
pipeline_tag: sentence-similarity
Encoder-model for search query similarity task.
Fast and accurate.
Sentencepiece tokenizer fitted on 269 million Russian search queries log.
DeBERTaV2 with a short context length to save the memory.
The data set for validation with manual markup consisted of 362 thousand examples.
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from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('fkrasnov2/SBE')
tokenizer = AutoTokenizer.from_pretrained('fkrasnov2/SBE')
input_ids = tokenizer.encode("чёрное платье", max_length=model.config.max_position_embeddings, truncation=True, return_tensors='pt')
model.eval()
vector = model(input_ids=input_ids, attention_mask=input_ids>3)[0][0,0]
assert model.config.hidden_size == vector.shape[0]