bert-base-japanese-v3
Maintainer: tohoku-nlp
43
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The bert-base-japanese-v3 model is a Japanese language model based on the BERT architecture, developed by the tohoku-nlp team. It is trained on a large corpus of Japanese text, including the Japanese portion of the CC-100 dataset and the Japanese Wikipedia. The model uses word-level tokenization based on the Unidic 2.1.2 dictionary, followed by WordPiece subword tokenization. It is trained with whole word masking, where all subword tokens corresponding to a single word are masked at once during pretraining.
This model can be compared to other Japanese BERT models like bert-base-japanese-whole-word-masking, which also uses whole word masking, and the multilingual bert-base-multilingual-uncased model, which covers 102 languages including Japanese.
Model inputs and outputs
Inputs
- Text: The
bert-base-japanese-v3model takes in Japanese text as input, which is first tokenized using the Unidic 2.1.2 dictionary and then split into subwords using the WordPiece algorithm.
Outputs
- Token representations: The model outputs contextual representations for each token in the input text, which can be used for a variety of downstream natural language processing tasks.
Capabilities
The bert-base-japanese-v3 model is a powerful language model that can be fine-tuned for a wide range of Japanese natural language processing tasks, such as text classification, named entity recognition, and question answering. Its whole word masking approach during pretraining allows the model to better capture the semantics of Japanese words, which are often composed of multiple characters.
What can I use it for?
The bert-base-japanese-v3 model can be used as a starting point for building Japanese language applications, such as:
- Text classification: Classify Japanese text into different categories (e.g., sentiment analysis, topic classification).
- Named entity recognition: Identify and extract named entities (e.g., people, organizations, locations) from Japanese text.
- Question answering: Build systems that can answer questions based on Japanese text passages.
To use the model, you can leverage the Hugging Face Transformers library, which provides easy-to-use APIs for fine-tuning and deploying BERT-based models.
Things to try
One interesting thing to try with the bert-base-japanese-v3 model is to compare its performance on Japanese language tasks to the performance of other Japanese language models, such as bert-base-japanese-whole-word-masking or the multilingual bert-base-multilingual-uncased model. This could help you understand the trade-offs and advantages of the different approaches to pretraining and tokenization used by these models.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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