### 📝 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](https://huggingface.co/datasets/samsum) **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)
```
{"language": ["ru"], "license": "cc", "tags": ["mbart"], "datasets": ["IlyaGusev/gazeta", "samsum", "samsum_(translated_into_Russian)"], "inference": {"parameters": {"no_repeat_ngram_size": "4,", "num_beams": 5}}, "widget": [{"text": "\u0414\u0436\u0435\u0444\u0444: \u041c\u043e\u0433\u0443 \u043b\u0438 \u044f \u043e\u0431\u0443\u0447\u0438\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u044c \ud83e\udd17 Transformers \u043d\u0430 Amazon SageMaker? \n\u0424\u0438\u043b\u0438\u043f\u043f: \u041a\u043e\u043d\u0435\u0447\u043d\u043e, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043d\u043e\u0432\u044b\u0439 \u043a\u043e\u043d\u0442\u0435\u0439\u043d\u0435\u0440 \u0434\u043b\u044f \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f HuggingFace. \n\u0414\u0436\u0435\u0444\u0444: \u0425\u043e\u0440\u043e\u0448\u043e.\n\u0414\u0436\u0435\u0444\u0444: \u0438 \u043a\u0430\u043a \u044f \u043c\u043e\u0433\u0443 \u043d\u0430\u0447\u0430\u0442\u044c? \n\u0414\u0436\u0435\u0444\u0444: \u0433\u0434\u0435 \u044f \u043c\u043e\u0433\u0443 \u043d\u0430\u0439\u0442\u0438 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e? \n\u0424\u0438\u043b\u0438\u043f\u043f: \u043e\u043a, \u043e\u043a, \u0437\u0434\u0435\u0441\u044c \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u0432\u0441\u0435: https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face\n"}], "model-index": [{"name": "mbart_ruDialogSum", "results": [{"task": {"type": "abstractive-text-summarization", "name": "Abstractive Dialogue Summarization"}, "dataset": {"name": "SAMSum Corpus (translated to Russian)", "type": "samsum"}, "metrics": [{"type": "rogue-1", "value": 34.5, "name": "Validation ROGUE-1"}, {"type": "rogue-l", "value": 33, "name": "Validation ROGUE-L"}, {"type": "rogue-1", "value": 31, "name": "Test ROGUE-1"}, {"type": "rogue-l", "value": 28, "name": "Test ROGUE-L"}]}]}]}
transformers pytorch mbart text2text-generation ru license:cc model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #ru #license-cc #model-index #autotrain_compatible #endpoints_compatible #region-us 

###  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

###  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
TAGS
#transformers #pytorch #mbart #text2text-generation #ru #license-cc #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ###  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
passage: TAGS
#transformers #pytorch #mbart #text2text-generation #ru #license-cc #model-index #autotrain_compatible #endpoints_compatible #region-us 
###  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