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

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.