readme: ---
  language:
  - rus
  tags:
  - mbart
  inference:
    parameters:
      no_repeat_ngram_size: 4,
      num_beams : 5
  datasets:
  - IlyaGusev/gazeta
  - samsum
  - samsum_(translated_into_Russian)
  widget:
  - text: | 
      Джефф: Могу ли я обучить модель 🤗 Transformers на Amazon SageMaker? 
      Филипп: Конечно, вы можете использовать новый контейнер для глубокого обучения HuggingFace. 
      Джефф: Хорошо.
      Джефф: и как я могу начать? 
      Джефф: где я могу найти документацию? 
      Филипп: ок, ок, здесь можно найти все: https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
  
  model-index:
  - name: "mbart_ruDialogSum"
    results:
    - task: 
        name: Abstractive Dialogue Summarization
        type: abstractive-text-summarization 
      dataset:
        name: "SAMSum Corpus (translated to Russian)" 
        type: samsum
      metrics:
         - name: Validation ROGUE-1
           type: rogue-1
           value: 34.5
         - name: Validation ROGUE-L
           type: rogue-l
           value: 33
         - name: Test ROGUE-1
           type: rogue-1
           value: 31
         - name: Test ROGUE-L
           type: rogue-l
           value: 28
  ---
  ### 📝 Description
  
  MBart for Russian summarization fine-tuned for **dialogues** summarization.
  
  
  This model was firstly fine-tuned by [Ilya Gusev](https://hf.co/IlyaGusev) on [Gazeta dataset](https://huggingface.co/datasets/IlyaGusev/gazeta). We have **fine tuned** that model on [SamSum dataset]() **translated to Russian** using GoogleTranslateAPI
  
  🤗 Moreover! We have implemented a **! telegram bot [@summarization_bot](https://t.me/summarization_bot) !** with the inference of this model. Add it to the chat and get summaries instead of dozens spam messages!  🤗
  
  
  ### ❓ How to use with code
  ```python
  from transformers import MBartTokenizer, MBartForConditionalGeneration
  
  # Download model and tokenizer
  model_name = "Kirili4ik/mbart_ruDialogSum"   
  tokenizer =  AutoTokenizer.from_pretrained(model_name)
  model = MBartForConditionalGeneration.from_pretrained(model_name)
  model.eval()
  
  article_text = "..."
  
  input_ids = tokenizer(
      [article_text],
      max_length=600,
      padding="max_length",
      truncation=True,
      return_tensors="pt",
  )["input_ids"]
  
  output_ids = model.generate(
      input_ids=input_ids,
      top_k=0,
      num_beams=3,
      no_repeat_ngram_size=3
  )[0]
  
  
  summary = tokenizer.decode(output_ids, skip_special_tokens=True)
  print(summary)
  ```
  