mt5_summarize_japanese
| Property | Value |
|---|---|
| Parameter Count | 300M |
| License | Apache 2.0 |
| Base Model | google/mt5-small |
| Best ROUGE1 Score | 46.25% |
What is mt5_summarize_japanese?
mt5_summarize_japanese is a specialized text summarization model fine-tuned for Japanese language content. Based on Google's mt5-small architecture, this model has been specifically trained on BBC news articles from the XL-Sum Japanese dataset to generate concise, accurate summaries of Japanese text.
Implementation Details
The model utilizes a transformer-based architecture with 300M parameters, trained using PyTorch. It employs a sequence-to-sequence approach, where the first sentence of news articles serves as the summary target while the remaining content forms the source text. The training process involved key hyperparameters including a learning rate of 0.0005, train batch size of 32, and linear scheduler with warmup steps.
- Trained for 10 epochs with gradient accumulation steps of 16
- Optimized using Adam optimizer with betas=(0.9,0.999)
- Achieves ROUGE scores: ROUGE1=0.4625, ROUGE2=0.2866, ROUGEL=0.3656
Core Capabilities
- Generates concise Japanese language summaries
- Optimized for news article summarization
- Supports batch processing with PyTorch backend
- Implements efficient text-to-text generation
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Japanese text summarization, trained on high-quality news content from BBC, making it particularly effective for summarizing formal Japanese news articles and similar content.
Q: What are the recommended use cases?
The model is best suited for summarizing news stories and formal Japanese text that includes events, background, results, and comments. It's not recommended for conversations, business documents, academic papers, or short tales as these weren't included in the training data.
