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--- |
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language: ["ru"] |
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tags: |
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- russian |
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- fill-mask |
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- pretraining |
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- embeddings |
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- masked-lm |
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- tiny |
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- feature-extraction |
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- sentence-similarity |
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license: mit |
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widget: |
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- text: "Миниатюрная модель для [MASK] разных задач." |
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--- |
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This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny): a small Russian BERT-based encoder with high-quality sentence embeddings. |
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The differences from the previous version include: |
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- a larger vocabulary: 83828 tokens instead of 29564; |
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- larger supported sequences: 2048 instead of 512; |
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- sentence embeddings approximate LaBSE closer than before; |
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- meaningful segment embeddings (tuned on the NLI task) |
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- the model is focused only on Russian. |
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The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task. |
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Sentence embeddings can be produced as follows: |
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```python |
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# pip install transformers sentencepiece |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2") |
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model = AutoModel.from_pretrained("cointegrated/rubert-tiny2") |
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# model.cuda() # uncomment it if you have a GPU |
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def embed_bert_cls(text, model, tokenizer): |
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t = tokenizer(text, padding=True, truncation=True, return_tensors='pt') |
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with torch.no_grad(): |
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model_output = model(**{k: v.to(model.device) for k, v in t.items()}) |
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embeddings = model_output.last_hidden_state[:, 0, :] |
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embeddings = torch.nn.functional.normalize(embeddings) |
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return embeddings[0].cpu().numpy() |
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print(embed_bert_cls('привет мир', model, tokenizer).shape) |
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# (312,) |
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``` |
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