README.md

metadata

language:
  - ru
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
license: cc

📝 Description

MBart for Russian summarization fine-tuned for dialogues summarization.

This model was firstly fine-tuned by Ilya Gusev on Gazeta dataset. We have fine tuned that model on SamSum dataset translated to Russian using GoogleTranslateAPI

🤗 Moreover! We have implemented a ! telegram bot @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

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)