Mt5 Summarize Japanese

🚀 mt5_summarize_japanese

This model is a fine - tuned version of [google/mt5 - small](https://huggingface.co/google/mt5 - small) for Japanese text summarization, offering an effective solution for quickly getting summaries of Japanese content.

🚀 Quick Start

This model is a fine - tuned version of [google/mt5 - small](https://huggingface.co/google/mt5 - small) trained for Japanese summarization. It is fine - tuned on BBC news articles (XL - Sum Japanese dataset), where the first sentence (headline sentence) is used as the summary and the rest as the article.

So, please fill news story (including, such as, event, background, result, and comment) as source text in the inferece widget. (Other corpra - such as, conversation, business document, academic paper, or short tale - are not seen in the training set.)

It achieves the following results on the evaluation set:

✨ Features

• Language Focus: Specialized for Japanese text summarization.
• Fine - Tuned: Based on the [google/mt5 - small](https://huggingface.co/google/mt5 - small) model, fine - tuned on specific Japanese news datasets.

💻 Usage Examples

Basic Usage

from transformers import pipeline

seq2seq = pipeline("summarization", model="tsmatz/mt5_summarize_japanese")
sample_text = "サッカーのワールドカップカタール大会、世界ランキング24位でグループEに属する日本は、23日の1次リーグ初戦において、世界11位で過去4回の優勝を誇るドイツと対戦しました。試合は前半、ドイツの一方的なペースではじまりましたが、後半、日本の森保監督は攻撃的な選手を積極的に動員して流れを変えました。結局、日本は前半に1点を奪われましたが、途中出場の堂安律選手と浅野拓磨選手が後半にゴールを決め、2対1で逆転勝ちしました。ゲームの流れをつかんだ森保采配が功を奏しました。"
result = seq2seq(sample_text)
print(result)

📚 Documentation

Training procedure

You can download the source code for fine - tuning from [here](https://github.com/tsmatz/huggingface - finetune - japanese/blob/master/02 - summarize.ipynb).

Training hyperparameters

The following hyperparameters were used during training:

Training results

Framework versions

📄 License

This model is licensed under the Apache - 2.0 license.