roberta_qa_japanese
| Property | Value |
|---|---|
| Parameter Count | 110M |
| License | MIT |
| Base Model | rinna/japanese-roberta-base |
| Training Dataset | SkelterLabsInc/JaQuAD |
What is roberta_qa_japanese?
roberta_qa_japanese is a specialized question-answering model built on RoBERTa architecture, specifically designed for Japanese language processing. It's a fine-tuned version of rinna's japanese-roberta-base model, optimized for extractive question answering tasks using the JaQuAD dataset.
Implementation Details
The model utilizes a sophisticated training procedure with carefully tuned hyperparameters, including a learning rate of 7e-05 and linear scheduler with warmup steps. Training was conducted over 3 epochs with a total batch size of 32, achieving impressive validation loss improvements from 1.0311 to 0.0516.
- Built on PyTorch framework with Transformers 4.23.1
- Implements efficient tokenization using AutoTokenizer
- Supports both pipeline and manual inference approaches
- Employs gradient accumulation for optimized training
Core Capabilities
- Extractive question answering for Japanese text
- Handles complex contextual understanding
- Supports variable-length inputs up to 318 tokens
- Provides confidence scores for answer spans
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Japanese language question answering, utilizing the robust RoBERTa architecture and being fine-tuned on human-annotated Wikipedia articles through the JaQuAD dataset. Its optimization for Japanese text sets it apart from generic multilingual models.
Q: What are the recommended use cases?
The model is ideal for applications requiring Japanese text comprehension and information extraction, such as automated customer service, document analysis, and educational tools. It performs best when used for extractive QA tasks where the answer is contained within the provided context.
