Update README.md

README.md

@@ -1,3 +1,42 @@
 ---
 license: apache-2.0
+language:
+- ru
+tags:
+- PyTorch
+- Tensorflow
+- Transformers
 ---
+
+# RU-ELECTRA medium model (cased) for Sentence Embeddings in Russian language.
+
+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/ru_s_electra_medium")
+model = AutoModel.from_pretrained("sberbank-ai/ru_s_electra_medium")
+#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'])
+```
+# Authors
++ SberDevices RnD Team
++ by Aleksandr Abramov: [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko)