README.md

---
language:
- ru
tags:
- summarization
- mbart
license: apache-2.0
---

# MBARTRuSumGazeta

## Model description

This is a ported version of [fairseq model](https://www.dropbox.com/s/fijtntnifbt9h0k/gazeta_mbart_v2_fairseq.tar.gz).

For more details, please see, [Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063).

## Intended uses & limitations

#### How to use

```python
from transformers import MBartTokenizer, MBartForConditionalGeneration

article_text = "..."
model_name = "IlyaGusev/mbart_ru_sum_gazeta"
tokenizer = MBartTokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)

input_ids = tokenizer.prepare_seq2seq_batch(
    [source],
    src_lang="en_XX",
    return_tensors="pt",
    padding="max_length",
    truncation=True,
    max_length=600
)["input_ids"][0]

output_ids = model.generate(
    input_ids=input_ids.unsqueeze(0),
    max_length=162,
    no_repeat_ngram_size=3,
    num_beams=5,
    top_k=0,
    decoder_start_token_id=tokenizer.lang_code_to_id["ru_RU"]
)[0]
summary = tokenizer.decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(summary)
```

#### Limitations and bias

- The model should work well with Gazeta.ru articles, but for any other agencies it can suffer from domain change


## Training data

- Dataset: https://github.com/IlyaGusev/gazeta

## Training procedure

- Fairseq training script: https://github.com/IlyaGusev/summarus/blob/master/external/bart_scripts/train.sh
- Porting: https://colab.research.google.com/drive/13jXOlCpArV-lm4jZQ0VgOpj6nFBYrLAr

## Eval results


### BibTeX entry and citation info

```bibtex
@InProceedings{10.1007/978-3-030-59082-6_9,
author="Gusev, Ilya",
editor="Filchenkov, Andrey
and Kauttonen, Janne
and Pivovarova, Lidia",
title="Dataset for Automatic Summarization of Russian News",
booktitle="Artificial Intelligence and Natural Language",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="122--134",
isbn="978-3-030-59082-6"
}
```