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Custom Dataset Tutorial

This tutorial is intended for temporary and informal use of datasets. If the dataset requires long-term use or has specific needs for custom reading/inference/evaluation, it is strongly recommended to implement it according to the methods described in new_dataset.md.

In this tutorial, we will introduce how to test a new dataset without implementing a config or modifying the OpenCompass source code. We support two types of tasks: multiple choice (mcq) and question & answer (qa). For mcq, both ppl and gen inferences are supported; for qa, gen inference is supported.

Dataset Format

We support datasets in both .jsonl and .csv formats.

Multiple Choice (`mcq`)

For mcq datasets, the default fields are as follows:

question: The stem of the multiple-choice question.
A, B, C, ...: Single uppercase letters representing the options, with no limit on the number. Defaults to parsing consecutive letters strating from A as options.
answer: The correct answer to the multiple-choice question, which must be one of the options used above, such as A, B, C, etc.

Non-default fields will be read in but are not used by default. To use them, specify in the .meta.json file.

An example of the .jsonl format:

{"question": "165+833+650+615=", "A": "2258", "B": "2263", "C": "2281", "answer": "B"}
{"question": "368+959+918+653+978=", "A": "3876", "B": "3878", "C": "3880", "answer": "A"}
{"question": "776+208+589+882+571+996+515+726=", "A": "5213", "B": "5263", "C": "5383", "answer": "B"}
{"question": "803+862+815+100+409+758+262+169=", "A": "4098", "B": "4128", "C": "4178", "answer": "C"}

An example of the .csv format:

question,A,B,C,answer
127+545+588+620+556+199=,2632,2635,2645,B
735+603+102+335+605=,2376,2380,2410,B
506+346+920+451+910+142+659+850=,4766,4774,4784,C
504+811+870+445=,2615,2630,2750,B

Question & Answer (`qa`)

For qa datasets, the default fields are as follows:

question: The stem of the question & answer question.
answer: The correct answer to the question & answer question. It can be missing, indicating the dataset has no correct answer.

Non-default fields will be read in but are not used by default. To use them, specify in the .meta.json file.

An example of the .jsonl format:

{"question": "752+361+181+933+235+986=", "answer": "3448"}
{"question": "712+165+223+711=", "answer": "1811"}
{"question": "921+975+888+539=", "answer": "3323"}
{"question": "752+321+388+643+568+982+468+397=", "answer": "4519"}

An example of the .csv format:

question,answer
123+147+874+850+915+163+291+604=,3967
149+646+241+898+822+386=,3142
332+424+582+962+735+798+653+214=,4700
649+215+412+495+220+738+989+452=,4170

Command Line List

Custom datasets can be directly called for evaluation through the command line.

python run.py \
    --models hf_llama2_7b \
    --custom-dataset-path xxx/test_mcq.csv \
    --custom-dataset-data-type mcq \
    --custom-dataset-infer-method ppl

python run.py \
    --models hf_llama2_7b \
    --custom-dataset-path xxx/test_qa.jsonl \
    --custom-dataset-data-type qa \
    --custom-dataset-infer-method gen

In most cases, --custom-dataset-data-type and --custom-dataset-infer-method can be omitted. OpenCompass will

set them based on the following logic:

If options like A, B, C, etc., can be parsed from the dataset file, it is considered an mcq dataset; otherwise, it is considered a qa dataset.
The default infer_method is gen.

Configuration File

In the original configuration file, simply add a new item to the datasets variable. Custom datasets can be mixed with regular datasets.

datasets = [
    {"path": "xxx/test_mcq.csv", "data_type": "mcq", "infer_method": "ppl"},
    {"path": "xxx/test_qa.jsonl", "data_type": "qa", "infer_method": "gen"},
]

Supplemental Information for Dataset `.meta.json`

OpenCompass will try to parse the input dataset file by default, so in most cases, the .meta.json file is not necessary. However, if the dataset field names are not the default ones, or custom prompt words are required, it should be specified in the .meta.json file.

The file is placed in the same directory as the dataset, with the filename followed by .meta.json. An example file structure is as follows:

.
├── test_mcq.csv
├── test_mcq.csv.meta.json
├── test_qa.jsonl
└── test_qa.jsonl.meta.json

Possible fields in this file include:

abbr (str): Abbreviation of the dataset, serving as its ID.
data_type (str): Type of dataset, options are mcq and qa.
infer_method (str): Inference method, options are ppl and gen.
human_prompt (str): User prompt template for generating prompts. Variables in the template are enclosed in {}, like {question}, {opt1}, etc. If template exists, this field will be ignored.
bot_prompt (str): Bot prompt template for generating prompts. Variables in the template are enclosed in {}, like {answer}, etc. If template exists, this field will be ignored.
template (str or dict): Question template for generating prompts. Variables in the template are enclosed in {}, like {question}, {opt1}, etc. The relevant syntax is in here regarding infer_cfg['prompt_template']['template'].
input_columns (list): List of input fields for reading data.
output_column (str): Output field for reading data.
options (list): List of options for reading data, valid only when data_type is mcq.

For example:

{
    "human_prompt": "Question: 127 + 545 + 588 + 620 + 556 + 199 =\nA. 2632\nB. 2635\nC. 2645\nAnswer: Let's think step by step, 127 + 545 + 588 + 620 + 556 + 199 = 672 + 588 + 620 + 556 + 199 = 1260 + 620 + 556 + 199 = 1880 + 556 + 199 = 2436 + 199 = 2635. So the answer is B.\nQuestion: {question}\nA. {A}\nB. {B}\nC. {C}\nAnswer: ",
    "bot_prompt": "{answer}"
}

{
    "template": "Question: {my_question}\nX. {X}\nY. {Y}\nZ. {Z}\nW. {W}\nAnswer:",
    "input_columns": ["my_question", "X", "Y", "Z", "W"],
    "output_column": "my_answer",
}

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