Model Card for tradenewssum-mbart

This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the TradeNewsSum dataset for multilingual abstractive summarization of foreign trade news in Russian and English.

Model Details

Model Description

This is a multilingual summarization model trained on economic and foreign trade news in Russian and English. It is based on the facebook/mbart-large-50-many-to-many-mmt architecture and fine-tuned specifically for the task of generating concise, informative summaries for news articles in the domain of international trade.

Model type: Sequence-to-sequence transformer (mBART)
Language(s) (NLP): Russian, English
License: MIT
Finetuned from model: facebook/mbart-large-50-many-to-many-mmt

Model Sources

Repository: https://huggingface.co/lyutovad/mbart50-tradenewssum
Dataset: https://huggingface.co/datasets/lyutovad/TradeNewsSum

Uses

How to Get Started with the Model

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

model = MBartForConditionalGeneration.from_pretrained("lyutovad/tradenewssum-mbart")
tokenizer = MBart50TokenizerFast.from_pretrained("lyutovad/tradenewssum-mbart")

text = "Ваш новостной текст здесь / Your news article goes here."
lang = "ru"  # or "en"
tokenizer.src_lang = "ru_RU" if lang == "ru" else "en_XX"

inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
generated_ids = model.generate(**inputs, max_length=256, num_beams=4)
summary = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(summary)

Direct Use

Generating abstractive summaries of foreign trade-related news in Russian and English.
Assisting analysts and journalists working with economic content.

Downstream Use

Could be integrated into news apps or research tools that require multilingual summarization.
Useful for training domain-specific summarization pipelines.

Out-of-Scope Use

Not suitable for general-purpose summarization outside the economic or trade domain.
Not intended for languages other than Russian and English.
Should not be used where factual precision is critical without human review, due to risk of hallucination.

Bias, Risks, and Limitations

As with most large language models, this model may:

Hallucinate or omit important factual details.
Be sensitive to domain shift — performs best on economic/trade texts.
Reflect biases present in the news sources used in the dataset.

Recommendations

Users should:

Apply human verification when using summaries in professional or sensitive settings.
Avoid use for non-economic domains without retraining.

Evaluation

Testing Data

Test split of the TradeNewsSum dataset.

Factors

Evaluated separately for Russian and English subsets.

Metrics

Language	ROUGE-1	ROUGE-2	ROUGE-L	ROUGE-Lsum	METEOR	BERTScore-F1	NER-F1
ru	0.5165	0.3646	0.5102	0.5093	0.7335	0.9533	0.707
en	0.6053	0.4640	0.5424	0.5427	0.5712	0.9344	0.643

ROUGE: Measures n-gram overlap between the generated summary and the reference.
METEOR: Takes into account synonyms and stemming.
BERTScore: Measures semantic similarity using contextual embeddings.
NER-F1: Measures preservation of named entities in summaries.

lyutovad
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mbart50-tradenewssum