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--- |
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license: mit |
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language: |
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- ru |
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tags: |
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- PyTorch |
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- Tensorflow |
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- Transformers |
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--- |
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# RU-ELECTRA medium model for Embeddings in Russian language. |
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For better quality, use mean token embeddings. |
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## Usage (HuggingFace Models Repository) |
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You can use the model directly from the model repository to compute sentence embeddings: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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#Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) |
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sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-10) |
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return sum_embeddings / sum_mask |
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#Sentences we want sentence embeddings for |
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sentences = ['Привет! Как твои дела?', |
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'А правда, что 42 твое любимое число?'] |
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#Load AutoModel from huggingface model repository |
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tokenizer = AutoTokenizer.from_pretrained("sberbank-ai/ruElectra-medium") |
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model = AutoModel.from_pretrained("sberbank-ai/ruElectra-medium") |
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#Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt') |
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#Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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#Perform pooling. In this case, mean pooling |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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``` |
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# Authors |
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+ [SaluteDevices](https://sberdevices.ru/) RnD Team. |
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+ by Aleksandr Abramov: [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko) |
