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--- |
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language: |
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- ja |
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tags: |
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- japanese |
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- text-generation |
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- gptj |
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- pytorch |
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- transformers |
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- t5tokenizer |
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- sentencepiece |
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license: apache-2.0 |
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--- |
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This pre-trained model is work in progress! Model weight download will be available in the future. |
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A 6.8 billion parameter pre-trained model for Japanese language, based on EleutherAI's Mesh Transformer JAX, that has a similar model structure to their GPT-J-6B pre-trained model. |
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EleutherAIによるMesh Transformer JAXをコードベースとした、GPT-J-6Bに似たストラクチャと約68.7億パラメータを持つ日本語pre-trainedモデルです。 |
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- We used T5Tokenizer and SentencePiece instead of GPT-2/3 tokenizer. Normalization done by SentencePiece is must for Japanese tokenizing as there are so much many more variations for common symbols than Western languages. |
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- Tokenizer has a vocabulary of 52,500 tokens and trained on Japanese Wikipedia dump as of 01 Aug 2021. |
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- The model fits within 16GB VRAM GPUs like P100 for inference up to 1688 context length. Full 2048 context length output requires 20GB VRAM or more (e.g. GTX3090/A5000). |
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- The model was trained with TPUv3-128 generously provided by Google TRC for about 4 weeks. We are currently formatting additional datasets and preparing for more training time. |
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## Specifications |
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| Hyperparameter | Value | |
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|-------------------|--------| |
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| n_parameters | 6,876,450,080 | |
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| n_layers | 32 | |
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| d_model | 4,096 | |
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| d_ff | 16,384 | |
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| n_heads | 16 | |
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| d_head | 256 | |
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| n_ctx | 2,048 | |
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| n_vocab | 52,512 | |
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| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) | |
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| RoPE dimensions | 64 | |
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## Instructions |
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We recommend to use finetuneanon's forked transformer codebase for inferencing as split checkpoint loads up a lot faster than monolithic checkpoint supported by HuggingFace Transformers repository. |
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The tokenizer still uses 50256 as the <|endoftext|> substitute. Therefore 50256 should be excluded when inferencing. |
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## Datasets |
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Lack of quality Japanese corpus was one of the major challenges when we trained the model. We aimed to compile well-formatted corpuses outside of Common Crawl. |
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The dataset is normalized and sanitized against leading and trailing spaces, excessive CR/LF repetitions. |
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The whole dataset is about 400GB (as of October 2021) and 106B tokens (compared to 825GB/300B tokens for The Pile). |
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** Common Crawl |
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- Jan-Dec 2018 72GB CC100-Japanese (https://metatext.io/datasets/cc100-japanese) |
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- November 2018 106GB OSCAR-Japanese (https://oscar-corpus.com) |
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- 75GB Converted 860GB Google C4 Multilingual Japanese (re-formatted) |
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** Books |
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- 140GB Web Fictions, non-fictions and blogs corpus |
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- 5GB Books and Aozora Bunko corpus (weighted 2x) |
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** News |
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- 1GB Scientific news, medical news and web news corpus |
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** Wikipedia |
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- Aug 2021 3GB Assorted and Deduplicated Japanese Wikipedia (weighted 2x) |
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- Aug 2021 Wikibooks, Wikinews, Wikiquote, Wikisource, Wiktionary, Wikiversity and Wikivoyage |
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** Other Corpuses |
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- 2018 OpenSubtitles (https://opus.nlpl.eu/OpenSubtitles-v2018.php) |
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- 80-90's BBS Logs |
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- Assorted Blogs Crawl |
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- QED-ja |
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- TED 2020-ja |
