| --- |
| language: |
| - ja |
| pretty_name: lambda-chat |
| license: other |
| task_categories: |
| - text-generation |
| tags: |
| - japanese |
| - instruction-following |
| - conversational |
| - parquet |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "train/*.parquet" |
| - split: validation |
| path: "validation.parquet" |
| - split: test |
| path: "test.parquet" |
| --- |
| |
| # lambda-chat |
|
|
| lambda-chat is a Japanese instruction-following dataset for supervised fine-tuning of chat models. It combines openly available datasets into one consistent chat format for easier use. |
|
|
| ## Purpose |
|
|
| The dataset is intended for training and evaluating Japanese chat and instruction-following models. Each example uses a list of messages with `role` and `content` fields. |
|
|
| ## Source Data |
|
|
| The dataset contains data from the following sources. |
|
|
| - [APTO-001/ja-safety-sft-dataset](https://huggingface.co/datasets/APTO-001/ja-safety-sft-dataset) |
| - [APTO-001/japanese-civil-law-llm-instruction-dataset](https://huggingface.co/datasets/APTO-001/japanese-civil-law-llm-instruction-dataset) |
| - [APTO-001/instruction-following-dataset](https://huggingface.co/datasets/APTO-001/instruction-following-dataset) |
| - [APTO-001/japanese-reasoning-dataset-sample](https://huggingface.co/datasets/APTO-001/japanese-reasoning-dataset-sample) |
| - [APTO-001/japanese-style-contrast-dataset](https://huggingface.co/datasets/APTO-001/japanese-style-contrast-dataset) |
| - [APTO-001/llm-safety-japanese-multiturn-dataset](https://huggingface.co/datasets/APTO-001/llm-safety-japanese-multiturn-dataset) |
| - [megagonlabs/instruction_ja](https://huggingface.co/datasets/megagonlabs/instruction_ja) |
| - [llm-jp/extraction-wiki-ja](https://huggingface.co/datasets/llm-jp/extraction-wiki-ja) |
| - [llm-jp/llm-jp-instructions](https://huggingface.co/datasets/llm-jp/llm-jp-instructions) |
| - [llm-jp/magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0) |
| - [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) |
|
|
| `llm-jp/llm-jp-instructions` and `APTO-001/ja-safety-sft-dataset` are published under `CC BY 4.0` license. |
|
|
| ## Data Processing |
|
|
| The source data was deduplicated, normalized with NFKC, and filtered to remove code and LaTeX content. The examples were then randomly shuffled and split into train, validation, and test sets. |
|
|
| ## Dataset Size |
|
|
| | Split | Rows | |
| | --- | ---: | |
| | train | 183,072 | |
| | validation | 1,868 | |
| | test | 1,868 | |
| | total | 186,808 | |
|
|
| The dataset contains about 50M assistant tokens measured by Gemma 4 12B tokenizer. |
|
|
| ## Columns |
|
|
| | Column | Description | |
| | --- | --- | |
| | `id` | Unique identifier for the example. | |
| | `messages` | Chat messages. Each message has `role` and `content`. | |
| | `output_char` | Number of characters in all assistant responses. | |
| | `output_token` | Number of tokens in all assistant responses. | |
| | `dataset_name` | Source dataset name | |
|
|
| ## Example |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("KeisukeMiyamoto/lambda-chat") |
| print(dataset["train"][0]) |
| ``` |
