keks28dima/rbc

MBARTRuSumGazeta

Model description

This is a ported version of fairseq model.

For more details, please see Dataset for Automatic Summarization of Russian News.

Intended uses & limitations

How to use

Colab: link

from transformers import MBartTokenizer, MBartForConditionalGeneration

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

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,
    no_repeat_ngram_size=4
)[0]

summary = tokenizer.decode(output_ids, skip_special_tokens=True)
print(summary)

Limitations and bias

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

Training data

• Dataset: Gazeta

Training procedure

• Fairseq training script: train.sh
• Porting: Colab link

Eval results

• Train dataset: Gazeta v1 train
• Test dataset: Gazeta v1 test
• Source max_length: 600
• Target max_length: 200
• no_repeat_ngram_size: 4
• num_beams: 5

Model	R-1-f	R-2-f	R-L-f	chrF	METEOR	BLEU	Avg char length
mbart_ru_sum_gazeta	32.4	14.3	28.0	39.7	26.4	12.1	371
rut5_base_sum_gazeta	32.2	14.4	28.1	39.8	25.7	12.3	330
rugpt3medium_sum_gazeta	26.2	7.7	21.7	33.8	18.2	4.3	244

• Train dataset: Gazeta v1 train
• Test dataset: Gazeta v2 test
• Source max_length: 600
• Target max_length: 200
• no_repeat_ngram_size: 4
• num_beams: 5

Model	R-1-f	R-2-f	R-L-f	chrF	METEOR	BLEU	Avg char length
mbart_ru_sum_gazeta	28.7	11.1	24.4	37.3	22.7	9.4	373
rut5_base_sum_gazeta	28.6	11.1	24.5	37.2	22.0	9.4	331
rugpt3medium_sum_gazeta	24.1	6.5	19.8	32.1	16.3	3.6	242

Predicting all summaries:

import json
import torch
from transformers import MBartTokenizer, MBartForConditionalGeneration
from datasets import load_dataset


def gen_batch(inputs, batch_size):
    batch_start = 0
    while batch_start < len(inputs):
        yield inputs[batch_start: batch_start + batch_size]
        batch_start += batch_size


def predict(
    model_name,
    input_records,
    output_file,
    max_source_tokens_count=600,
    batch_size=4
):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    tokenizer = MBartTokenizer.from_pretrained(model_name)
    model = MBartForConditionalGeneration.from_pretrained(model_name).to(device)
    
    predictions = []
    for batch in gen_batch(inputs, batch_size):
        texts = [r["text"] for r in batch]
        input_ids = tokenizer(
            batch,
            return_tensors="pt",
            padding="max_length",
            truncation=True,
            max_length=max_source_tokens_count
        )["input_ids"].to(device)
        
        output_ids = model.generate(
            input_ids=input_ids,
            no_repeat_ngram_size=4
        )
        summaries = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
        for s in summaries:
            print(s)
        predictions.extend(summaries)
    with open(output_file, "w") as w:
        for p in predictions:
            w.write(p.strip().replace("\n", " ") + "\n")

gazeta_test = load_dataset('IlyaGusev/gazeta', script_version="v1.0")["test"]
predict("IlyaGusev/mbart_ru_sum_gazeta", list(gazeta_test), "mbart_predictions.txt")

Evaluation: https://github.com/IlyaGusev/summarus/blob/master/evaluate.py

Flags: --language ru --tokenize-after --lower

BibTeX entry and citation info

@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"
}