readme

README.md

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+BERT large model (uncased) for Sentence Embeddings in Russian language.
+The model is described in this article](https://habr.com/ru/company/sberdevices/blog/527576/)
+For better quality, use mean token embeddings.
+
+## Usage (HuggingFace Models Repository)
+
+You can use the model directly from the model repository to compute sentence embeddings:
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return sum_embeddings / sum_mask
+
+
+
+#Sentences we want sentence embeddings for
+sentences = ['Привет! Как твои дела?',
+             'А правда, что 42 твое любимое число?']
+
+#Load AutoModel from huggingface model repository
+tokenizer = AutoTokenizer.from_pretrained("sberbank-ai/sbert_large_nlu_ru")
+model = AutoModel.from_pretrained("sberbank-ai/sbert_large_nlu_ru")
+
+#Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt')
+
+#Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+#Perform pooling. In this case, mean pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+```