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--- |
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language: |
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- ru |
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tags: |
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- summarization |
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license: apache-2.0 |
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--- |
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# RuT5TelegramHeadlines |
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## Model description |
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Based on [rut5-base](https://huggingface.co/cointegrated/rut5-base) model |
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## Intended uses & limitations |
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#### How to use |
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```python |
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from transformers import AutoTokenizer, T5ForConditionalGeneration |
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model_name = "IlyaGusev/rut5_telegram_headlines" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = T5ForConditionalGeneration.from_pretrained(model_name) |
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article_text = "..." |
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input_ids = tokenizer( |
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[article_text], |
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max_length=600, |
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add_special_tokens=True, |
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padding="max_length", |
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truncation=True, |
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return_tensors="pt" |
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)["input_ids"] |
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output_ids = model.generate( |
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input_ids=input_ids, |
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no_repeat_ngram_size=4 |
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)[0] |
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headline = tokenizer.decode(output_ids, skip_special_tokens=True) |
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print(headline) |
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``` |
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## Training data |
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- Dataset: [ru_all_split.tar.gz](https://www.dropbox.com/s/ykqk49a8avlmnaf/ru_all_split.tar.gz) |
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## Training procedure |
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- Training script: [train.py](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py) |
