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--- |
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language: "ru" |
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tags: |
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- paraphrasing |
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- russian |
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license: mit |
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--- |
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This is a small Russian paraphraser based on the [google/mt5-small](https://huggingface.co/google/mt5-small) model. |
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It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks. |
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This model was created by taking the [alenusch/mt5small-ruparaphraser](https://huggingface.co/alenusch/mt5small-ruparaphraser) model and stripping 96% of its vocabulary which is unrelated to the Russian language or infrequent. |
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* The original model has 300M parameters, with 256M of them being input and output embeddings. |
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* After shrinking the `sentencepiece` vocabulary from 250K to 20K the number of model parameters reduced to 65M parameters, and model size reduced from 1.1GB to 246MB. |
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* The first 5K tokens in the new vocabulary are taken from the original `mt5-small`. |
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* The next 15K tokens are the most frequent tokens obtained by tokenizing a Russian web corpus from the [Leipzig corpora collection](https://wortschatz.uni-leipzig.de/en/download/Russian). |
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The model can be used as follows: |
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``` |
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# !pip install transformers sentencepiece |
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import torch |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small") |
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model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small") |
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text = 'Ехал Грека через реку, видит Грека в реке рак. ' |
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inputs = tokenizer(text, return_tensors='pt') |
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with torch.no_grad(): |
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hypotheses = model.generate( |
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**inputs, |
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do_sample=True, top_p=0.95, num_return_sequences=10, |
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repetition_penalty=2.5, |
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max_length=32, |
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) |
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for h in hypotheses: |
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print(tokenizer.decode(h, skip_special_tokens=True)) |
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``` |
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