115 lines
4.3 KiB
Markdown
115 lines
4.3 KiB
Markdown
If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.json`.
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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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"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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"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": {
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"prompt": "the column name in the dataset containing the prompts. (default: instruction, for alpaca)",
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"query": "the column name in the dataset containing the queries. (default: input, for alpaca)",
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"response": "the column name in the dataset containing the responses. (default: output, for alpaca)",
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"history": "the column name in the dataset containing the histories. (default: None, for alpaca)",
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"messages": "the column name in the dataset containing the messages. (default: conversations, for sharegpt)",
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"role": "the key in the message represents the identity. (default: from, for sharegpt)",
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"content": "the key in the message represents the content. (default: value, for sharegpt)",
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"system": "the column name in the dataset containing the system prompts. (default: None, for both)"
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}
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}
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```
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Given above, you can use the custom dataset via specifying `--dataset dataset_name`.
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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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```json
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[
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{
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"instruction": "user instruction (required)",
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"input": "user input (optional)",
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"output": "model response (required)",
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"system": "system prompt (optional)",
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"history": [
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["user instruction in the first round (optional)", "model response in the first round (optional)"],
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["user instruction in the second round (optional)", "model response in the second round (optional)"]
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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 `columns` in `dataset_info.json` should be:
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```json
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"dataset_name": {
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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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"system": "system",
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"history": "history"
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}
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}
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```
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where the `prompt` and `response` columns should contain non-empty values, represent instruction and response respectively. The `query` column will be concatenated with the `prompt` column and used as input for the model.
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The `system` column will be used as the system prompt in the template. The `history` column is a list consisting string tuples representing query-response pairs in history. Note that the responses **in each round will be used for training**.
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For the pre-training datasets, only the `prompt` column will be used for training.
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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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```json
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{
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"instruction": "user instruction",
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"input": "user input",
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"output": [
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"chosen answer",
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"rejected answer"
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]
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}
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```
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The dataset in sharegpt format should follow the below format:
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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": "user 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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"system": "system prompt (optional)"
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}
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]
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```
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Regarding the above dataset, the `columns` in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"columns": {
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"messages": "conversations",
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"role": "from",
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"content": "value",
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"system": "system"
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}
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}
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```
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where the `messages` column should be a list whose length is even, and follow the `u/a/u/a/u/a` order.
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Pre-training datasets and preference datasets are incompatible with the sharegpt format yet.
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