update data readme
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The `dataset_info.json` contains all available datasets. If you are using a custom dataset, please make sure to add a *dataset description* in `dataset_info.json` and specify `dataset: dataset_name` before training to use it.
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The [dataset_info.json](dataset_info.json) contains all available datasets. If you are using a custom dataset, please **make sure** to add a *dataset description* in `dataset_info.json` and specify `dataset: dataset_name` before training to use it.
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Currently we support datasets in **alpaca** and **sharegpt** format.
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@ -41,11 +41,13 @@ Currently we support datasets in **alpaca** and **sharegpt** format.
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### Supervised Fine-Tuning Dataset
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* [Example dataset](alpaca_en_demo.json)
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In supervised fine-tuning, the `instruction` column will be concatenated with the `input` column and used as the human prompt, then the human prompt would be `instruction\ninput`. The `output` column represents the model response.
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The `system` column will be used as the system prompt if specified.
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The `history` column is a list consisting string tuples representing prompt-response pairs in the history messages. Note that the responses in the history **will also be learned by the model** in supervised fine-tuning.
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The `history` column is a list consisting of string tuples representing prompt-response pairs in the history messages. Note that the responses in the history **will also be learned by the model** in supervised fine-tuning.
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```json
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[
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@ -79,7 +81,9 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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### Pre-training Dataset
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In pre-training, only the `prompt` column will be used for model learning.
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- [Example dataset](c4_demo.json)
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In pre-training, only the `text` column will be used for model learning.
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```json
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[
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@ -133,6 +137,8 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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### KTO Dataset
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- [Example dataset](kto_en_demo.json)
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KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
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```json
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@ -162,7 +168,9 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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### Multimodal Dataset
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Multimodal datasets require a `images` column containing the paths to the input image. Currently we only support one image.
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- [Example dataset](mllm_demo.json)
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Multimodal datasets require a `images` column containing the paths to the input images. Currently we only support one image.
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```json
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[
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@ -195,7 +203,9 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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### Supervised Fine-Tuning Dataset
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Compared to the alpaca format, the sharegpt format allows the datasets have more **roles**, such as human, gpt, observation and function. They are presented in a list of objects in the `conversations` column.
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- [Example dataset](glaive_toolcall_en_demo.json)
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Compared to the alpaca format, the sharegpt format allows the datasets have **more roles**, such as human, gpt, observation and function. They are presented in a list of objects in the `conversations` column.
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Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions.
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@ -208,12 +218,12 @@ Note that the human and observation should appear in odd positions, while gpt an
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"value": "human instruction"
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},
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{
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"from": "gpt",
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"value": "model response"
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"from": "function_call",
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"value": "tool arguments"
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},
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{
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"from": "human",
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"value": "human instruction"
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"from": "observation",
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"value": "tool result"
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},
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{
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"from": "gpt",
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@ -242,6 +252,8 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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### Preference Dataset
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- [Example dataset](dpo_en_demo.json)
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Preference datasets in sharegpt format also require a better message in `chosen` column and a worse message in `rejected` column.
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```json
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`dataset_info.json` 包含了所有可用的数据集。如果您希望使用自定义数据集，请务必在 `dataset_info.json` 文件中添加*数据集描述*，并通过修改 `dataset: 数据集名称` 配置来使用数据集。
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[dataset_info.json](dataset_info.json) 包含了所有可用的数据集。如果您希望使用自定义数据集，请**务必**在 `dataset_info.json` 文件中添加*数据集描述*，并通过修改 `dataset: 数据集名称` 配置来使用数据集。
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目前我们支持 **alpaca** 格式和 **sharegpt** 格式的数据集。
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@ -41,6 +41,8 @@
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### 指令监督微调数据集
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- [样例数据集](alpaca_zh_demo.json)
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在指令监督微调时，`instruction` 列对应的内容会与 `input` 列对应的内容拼接后作为人类指令，即人类指令为 `instruction\ninput`。而 `output` 列对应的内容为模型回答。
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如果指定，`system` 列对应的内容将被作为系统提示词。
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@ -79,7 +81,9 @@
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### 预训练数据集
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对于**预训练数据集**，仅 `prompt` 列中的内容会用于模型学习，例如：
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- [样例数据集](c4_demo.json)
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在预训练时，只有 `text` 列中的内容会用于模型学习。
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```json
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[
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@ -133,6 +137,8 @@
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### KTO 数据集
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- [样例数据集](kto_en_demo.json)
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KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人类反馈。
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```json
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@ -162,6 +168,8 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
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### 多模态数据集
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- [样例数据集](mllm_demo.json)
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多模态数据集需要额外添加一个 `images` 列，包含输入图像的路径。目前我们仅支持单张图像输入。
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```json
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@ -195,9 +203,11 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
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### 指令监督微调数据集
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相比 alpaca 格式的数据集，sharegpt 格式支持更多的**角色种类**，例如 human、gpt、observation、function 等等。它们构成一个对象列表呈现在 `conversations` 列中。
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- [样例数据集](glaive_toolcall_zh_demo.json)
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其中 human 和 observation 必须出现在奇数位置，gpt 和 function 必须出现在偶数位置。
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相比 alpaca 格式的数据集，sharegpt 格式支持**更多的角色种类**，例如 human、gpt、observation、function 等等。它们构成一个对象列表呈现在 `conversations` 列中。
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注意其中 human 和 observation 必须出现在奇数位置，gpt 和 function 必须出现在偶数位置。
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```json
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[
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@ -208,12 +218,12 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
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"value": "人类指令"
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},
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{
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"from": "gpt",
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"value": "模型回答"
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"from": "function_call",
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"value": "工具参数"
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},
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{
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"from": "human",
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"value": "人类指令"
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"from": "observation",
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"value": "工具结果"
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},
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{
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"from": "gpt",
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@ -236,18 +246,14 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
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"messages": "conversations",
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"system": "system",
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"tools": "tools"
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},
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"tags": {
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"role_tag": "from",
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"content_tag": "value",
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"user_tag": "human",
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"assistant_tag": "gpt"
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}
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}
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```
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### 偏好数据集
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- [样例数据集](dpo_zh_demo.json)
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Sharegpt 格式的偏好数据集同样需要在 `chosen` 列中提供更优的消息，并在 `rejected` 列中提供更差的消息。
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```json
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