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data/README.md
@ -1,16 +1,17 @@
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If you are using a custom dataset, please add your **dataset description** to `dataset_info.json` according to the following format. We also provide several examples in the next section.
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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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Currently we support datasets in **alpaca** and **sharegpt** format.
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```json
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"dataset_name": {
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"hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url and file_name)",
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"ms_hub_url": "the name of the dataset repository on the ModelScope hub. (if specified, ignore script_url and file_name)",
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"ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url and file_name)",
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"script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name)",
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"file_name": "the name of the dataset file in this directory. (required if above are not specified)",
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"file_sha1": "the SHA-1 hash value of the dataset file. (optional, does not affect training)",
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"file_name": "the name of the dataset folder or dataset file in this directory. (required if above are not specified)",
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"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
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"ranking": "whether the dataset is a preference dataset or not. (default: False)",
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"subset": "the name of the subset. (optional, default: None)",
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"folder": "the name of the folder of the dataset repository on the Hugging Face hub. (optional, default: None)",
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"ranking": "whether the dataset is a preference dataset or not. (default: false)",
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"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
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"columns (optional)": {
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"prompt": "the column name in the dataset containing the prompts. (default: instruction)",
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"query": "the column name in the dataset containing the queries. (default: input)",
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@ -36,11 +37,15 @@ If you are using a custom dataset, please add your **dataset description** to `d
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}
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```
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After that, you can load the custom dataset by specifying `--dataset dataset_name`.
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## Alpaca Format
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----
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### Supervised Fine-Tuning Dataset
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Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format:
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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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```json
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[
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@ -57,7 +62,7 @@ Currently we support dataset in **alpaca** or **sharegpt** format, the dataset i
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]
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```
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Regarding the above dataset, the description in `dataset_info.json` should be:
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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@ -72,11 +77,9 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
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}
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```
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The `query` column will be concatenated with the `prompt` column and used as the human prompt, then the human prompt would be `prompt\nquery`. The `response` column represents the model response.
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### Pre-training Dataset
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The `system` column will be used as the system prompt. The `history` column is a list consisting string tuples representing prompt-response pairs in the history. Note that the responses in the history **will also be used for training** in supervised fine-tuning.
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For the **pre-training datasets**, only the `prompt` column will be used for training, for example:
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In pre-training, only the `prompt` column will be used for model learning.
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```json
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[
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@ -85,7 +88,7 @@ For the **pre-training datasets**, only the `prompt` column will be used for tra
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]
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```
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Regarding the above dataset, the description in `dataset_info.json` should be:
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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@ -96,20 +99,24 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
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}
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```
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For the **preference datasets**, the `response` column should be a string list whose length is 2, with the preferred answers appearing first, for example:
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### Preference Dataset
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Preference datasets are used for reward modeling, DPO training and ORPO training.
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It requires a better response in `chosen` column and a worse response in `rejected` column.
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```json
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[
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{
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"instruction": "human instruction",
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"input": "human input",
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"chosen": "chosen answer",
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"rejected": "rejected answer"
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"instruction": "human instruction (required)",
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"input": "human input (optional)",
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"chosen": "chosen answer (required)",
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"rejected": "rejected answer (required)"
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}
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]
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```
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Regarding the above dataset, the description in `dataset_info.json` should be:
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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@ -124,14 +131,86 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
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}
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```
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----
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### KTO Dataset
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The dataset in **sharegpt** format should follow the below format:
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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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[
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{
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"instruction": "human instruction (required)",
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"input": "human input (optional)",
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"output": "model response (required)",
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"kto_tag": "human feedback [true/false] (required)"
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"response": "output",
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"kto_tag": "kto_tag"
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}
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}
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```
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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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```json
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[
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{
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"instruction": "human instruction (required)",
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"input": "human input (optional)",
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"output": "model response (required)",
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"images": [
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"image path (required)"
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]
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"response": "output",
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"images": "images"
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}
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}
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```
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## Sharegpt Format
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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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Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions.
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```json
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[
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{
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"conversations": [
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{
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"from": "human",
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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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},
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{
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"from": "human",
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"value": "human instruction"
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@ -147,7 +226,7 @@ The dataset in **sharegpt** format should follow the below format:
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]
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```
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Regarding the above dataset, the description in `dataset_info.json` should be:
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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@ -157,19 +236,61 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
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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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where the `messages` column should be a list following the `u/a/u/a/u/a` order.
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### Preference Dataset
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We also supports the dataset in the **openai** format:
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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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[
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{
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"conversations": [
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{
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"from": "human",
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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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},
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{
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"from": "human",
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"value": "human instruction"
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}
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],
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"chosen": {
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"from": "gpt",
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"value": "chosen answer (required)"
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},
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"rejected": {
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"from": "gpt",
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"value": "rejected answer (required)"
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}
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"formatting": "sharegpt",
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"ranking": true,
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"columns": {
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"messages": "conversations",
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"chosen": "chosen",
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"rejected": "rejected"
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}
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}
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```
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### OpenAI Format
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The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
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```json
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[
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@ -192,7 +313,7 @@ We also supports the dataset in the **openai** format:
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]
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```
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Regarding the above dataset, the description in `dataset_info.json` should be:
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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@ -211,4 +332,6 @@ Regarding the above dataset, the description in `dataset_info.json` should be:
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}
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```
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Pre-training datasets and preference datasets are **incompatible** with the sharegpt format yet.
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The KTO datasets and multimodal datasets in sharegpt format are similar to the alpaca format.
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Pre-training datasets are **incompatible** with the sharegpt format.
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@ -1,4 +1,6 @@
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如果您使用自定义数据集，请务必按照以下格式在 `dataset_info.json` 文件中添加**数据集描述**。我们在下面也提供了一些例子。
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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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目前我们支持 **alpaca** 格式和 **sharegpt** 格式的数据集。
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```json
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"数据集名称": {
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@ -6,11 +8,10 @@
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"ms_hub_url": "ModelScope 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
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"script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略 file_name）",
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"file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
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"file_sha1": "数据集文件的 SHA-1 哈希值（可选，留空不影响训练）",
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"formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
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"ranking": "是否为偏好数据集（可选，默认：False）",
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"subset": "数据集子集的名称（可选，默认：None）",
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"folder": "Hugging Face 仓库的文件夹名称（可选，默认：None）",
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"ranking": "是否为偏好数据集（可选，默认：False）",
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"formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
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"columns（可选）": {
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"prompt": "数据集代表提示词的表头名称（默认：instruction）",
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"query": "数据集代表请求的表头名称（默认：input）",
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@ -20,8 +21,8 @@
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"system": "数据集代表系统提示的表头名称（默认：None）",
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"tools": "数据集代表工具描述的表头名称（默认：None）",
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"images": "数据集代表图像输入的表头名称（默认：None）",
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"chosen": "数据集代表更优回复的表头名称（默认：None）",
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"rejected": "数据集代表更差回复的表头名称（默认：None）",
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"chosen": "数据集代表更优回答的表头名称（默认：None）",
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"rejected": "数据集代表更差回答的表头名称（默认：None）",
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"kto_tag": "数据集代表 KTO 标签的表头名称（默认：None）"
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},
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"tags（可选，用于 sharegpt 格式）": {
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@ -31,16 +32,20 @@
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"assistant_tag": "消息中代表助手的 role_tag（默认：gpt）",
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"observation_tag": "消息中代表工具返回结果的 role_tag（默认：observation）",
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"function_tag": "消息中代表工具调用的 role_tag（默认：function_call）",
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"system_tag": "消息中代表系统提示的 role_tag（默认：system，会覆盖 system 列）"
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"system_tag": "消息中代表系统提示的 role_tag（默认：system，会覆盖 system column）"
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}
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}
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```
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然后，可通过使用 `--dataset 数据集名称` 参数加载自定义数据集。
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## Alpaca 格式
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----
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### 指令监督微调数据集
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该项目目前支持两种格式的数据集：**alpaca** 和 **sharegpt**，其中 alpaca 格式的数据集按照以下方式组织：
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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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`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮对话的指令和回答。注意在指令监督微调时，历史消息中的回答内容**也会被用于模型学习**。
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```json
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[
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@ -57,7 +62,7 @@
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]
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```
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对于上述格式的数据，`dataset_info.json` 中的描述应为：
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对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
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```json
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"数据集名称": {
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@ -72,11 +77,9 @@
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}
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```
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其中 `query` 列对应的内容会与 `prompt` 列对应的内容拼接后作为人类指令，即人类指令为 `prompt\nquery`。`response` 列对应的内容为模型回答。
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### 预训练数据集
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`system` 列对应的内容将被作为系统提示词。`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮的指令和回答。注意在指令监督学习时，历史消息中的回答**也会被用于训练**。
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对于**预训练数据集**，仅 `prompt` 列中的内容会用于模型训练，例如：
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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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```json
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[
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@ -85,7 +88,7 @@
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]
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```
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对于上述格式的数据，`dataset_info.json` 中的描述应为：
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对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
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```json
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"数据集名称": {
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@ -96,20 +99,24 @@
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}
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```
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对于**偏好数据集**，`response` 列应当是一个长度为 2 的字符串列表，排在前面的代表更优的回答，例如：
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### 偏好数据集
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偏好数据集用于奖励模型训练、DPO 训练和 ORPO 训练。
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它需要在 `chosen` 列中提供更优的回答，并在 `rejected` 列中提供更差的回答。
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```json
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[
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{
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"instruction": "人类指令",
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"input": "人类输入",
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"chosen": "优质回答",
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"rejected": "劣质回答"
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"instruction": "人类指令（必填）",
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"input": "人类输入（选填）",
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"chosen": "优质回答（必填）",
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"rejected": "劣质回答（必填）"
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}
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]
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```
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|
||||
对于上述格式的数据，`dataset_info.json` 中的描述应为：
|
||||
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
|
||||
|
||||
```json
|
||||
"数据集名称": {
|
||||
@ -124,14 +131,86 @@
|
||||
}
|
||||
```
|
||||
|
||||
----
|
||||
### KTO 数据集
|
||||
|
||||
而 **sharegpt** 格式的数据集按照以下方式组织：
|
||||
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。
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"instruction": "人类指令（必填）",
|
||||
"input": "人类输入（选填）",
|
||||
"output": "模型回答（必填）",
|
||||
"kto_tag": "人类反馈 [true/false]（必填）"
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
|
||||
|
||||
```json
|
||||
"数据集名称": {
|
||||
"file_name": "data.json",
|
||||
"columns": {
|
||||
"prompt": "instruction",
|
||||
"query": "input",
|
||||
"response": "output",
|
||||
"kto_tag": "kto_tag"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 多模态数据集
|
||||
|
||||
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。
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"instruction": "人类指令（必填）",
|
||||
"input": "人类输入（选填）",
|
||||
"output": "模型回答（必填）",
|
||||
"images": [
|
||||
"图像路径（必填）"
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
|
||||
|
||||
```json
|
||||
"数据集名称": {
|
||||
"file_name": "data.json",
|
||||
"columns": {
|
||||
"prompt": "instruction",
|
||||
"query": "input",
|
||||
"response": "output",
|
||||
"images": "images"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Sharegpt 格式
|
||||
|
||||
### 指令监督微调数据集
|
||||
|
||||
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。
|
||||
|
||||
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。
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"conversations": [
|
||||
{
|
||||
"from": "human",
|
||||
"value": "人类指令"
|
||||
},
|
||||
{
|
||||
"from": "gpt",
|
||||
"value": "模型回答"
|
||||
},
|
||||
{
|
||||
"from": "human",
|
||||
"value": "人类指令"
|
||||
@ -147,7 +226,7 @@
|
||||
]
|
||||
```
|
||||
|
||||
对于上述格式的数据，`dataset_info.json` 中的描述应为：
|
||||
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
|
||||
|
||||
```json
|
||||
"数据集名称": {
|
||||
@ -167,9 +246,57 @@
|
||||
}
|
||||
```
|
||||
|
||||
其中 `messages` 列应当是一个列表，且符合 `人类/模型/人类/模型/人类/模型` 的顺序。
|
||||
### 偏好数据集
|
||||
|
||||
我们同样支持 **openai** 格式的数据集：
|
||||
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。
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"conversations": [
|
||||
{
|
||||
"from": "human",
|
||||
"value": "人类指令"
|
||||
},
|
||||
{
|
||||
"from": "gpt",
|
||||
"value": "模型回答"
|
||||
},
|
||||
{
|
||||
"from": "human",
|
||||
"value": "人类指令"
|
||||
}
|
||||
],
|
||||
"chosen": {
|
||||
"from": "gpt",
|
||||
"value": "优质回答"
|
||||
},
|
||||
"rejected": {
|
||||
"from": "gpt",
|
||||
"value": "劣质回答"
|
||||
}
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
|
||||
|
||||
```json
|
||||
"数据集名称": {
|
||||
"file_name": "data.json",
|
||||
"formatting": "sharegpt",
|
||||
"ranking": true,
|
||||
"columns": {
|
||||
"messages": "conversations",
|
||||
"chosen": "chosen",
|
||||
"rejected": "rejected"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### OpenAI 格式
|
||||
|
||||
OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消息可能是系统提示词。
|
||||
|
||||
```json
|
||||
[
|
||||
@ -192,7 +319,7 @@
|
||||
]
|
||||
```
|
||||
|
||||
对于上述格式的数据，`dataset_info.json` 中的描述应为：
|
||||
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
|
||||
|
||||
```json
|
||||
"数据集名称": {
|
||||
@ -211,4 +338,6 @@
|
||||
}
|
||||
```
|
||||
|
||||
预训练数据集和偏好数据集**尚不支持** sharegpt 格式。
|
||||
Sharegpt 格式中的 KTO 数据集和多模态数据集与 alpaca 格式的类似。
|
||||
|
||||
预训练数据集**不支持** sharegpt 格式。
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user