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* add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * udpate dataset for modelscope support * update readme * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * update readme * remove tydiqa japanese subset * add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * update readme * udpate dataset for modelscope support * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * remove tydiqa japanese subset * update util * remove .DS_Store * fix md format * move util into package * update docs/get_started.md * restore eval_api_zhipu_v2.py, add environment setting * Update dataset * Update * Update * Update * Update --------- Co-authored-by: Yun lin <yunlin@U-Q9X2K4QV-1904.local> Co-authored-by: Yunnglin <mao.looper@qq.com> Co-authored-by: Yun lin <yunlin@laptop.local> Co-authored-by: Yunnglin <maoyl@smail.nju.edu.cn> Co-authored-by: zhangsongyang <zhangsongyang@pjlab.org.cn>
84 lines
3.5 KiB
Markdown
84 lines
3.5 KiB
Markdown
# Configure Datasets
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This tutorial mainly focuses on selecting datasets supported by OpenCompass and preparing their configs files. Please make sure you have downloaded the datasets following the steps in [Dataset Preparation](../get_started/installation.md#dataset-preparation).
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## Directory Structure of Dataset Configuration Files
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First, let's introduce the structure under the `configs/datasets` directory in OpenCompass, as shown below:
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```
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configs/datasets/
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├── agieval
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├── apps
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├── ARC_c
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├── ...
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├── CLUE_afqmc # dataset
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│ ├── CLUE_afqmc_gen_901306.py # different version of config
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│ ├── CLUE_afqmc_gen.py
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│ ├── CLUE_afqmc_ppl_378c5b.py
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│ ├── CLUE_afqmc_ppl_6507d7.py
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│ ├── CLUE_afqmc_ppl_7b0c1e.py
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│ └── CLUE_afqmc_ppl.py
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├── ...
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├── XLSum
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├── Xsum
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└── z_bench
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```
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In the `configs/datasets` directory structure, we flatten all datasets directly, and there are multiple dataset configurations within the corresponding folders for each dataset.
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The naming of the dataset configuration file is made up of `{dataset name}_{evaluation method}_{prompt version number}.py`. For example, `CLUE_afqmc/CLUE_afqmc_gen_db509b.py`, this configuration file is the `CLUE_afqmc` dataset under the Chinese universal ability, the corresponding evaluation method is `gen`, i.e., generative evaluation, and the corresponding prompt version number is `db509b`; similarly, `CLUE_afqmc_ppl_00b348.py` indicates that the evaluation method is `ppl`, i.e., discriminative evaluation, and the prompt version number is `00b348`.
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In addition, files without a version number, such as: `CLUE_afqmc_gen.py`, point to the latest prompt configuration file of that evaluation method, which is usually the most accurate prompt.
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## Dataset Selection
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In each dataset configuration file, the dataset will be defined in the `{}_datasets` variable, such as `afqmc_datasets` in `CLUE_afqmc/CLUE_afqmc_gen_db509b.py`.
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```python
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afqmc_datasets = [
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dict(
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abbr="afqmc-dev",
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type=AFQMCDatasetV2,
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path="./data/CLUE/AFQMC/dev.json",
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reader_cfg=afqmc_reader_cfg,
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infer_cfg=afqmc_infer_cfg,
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eval_cfg=afqmc_eval_cfg,
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),
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]
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```
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And `cmnli_datasets` in `CLUE_cmnli/CLUE_cmnli_ppl_b78ad4.py`.
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```python
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cmnli_datasets = [
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dict(
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type=HFDataset,
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abbr='cmnli',
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path='json',
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split='train',
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data_files='./data/CLUE/cmnli/cmnli_public/dev.json',
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reader_cfg=cmnli_reader_cfg,
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infer_cfg=cmnli_infer_cfg,
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eval_cfg=cmnli_eval_cfg)
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]
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```
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Take these two datasets as examples. If users want to evaluate these two datasets at the same time, they can create a new configuration file in the `configs` directory. We use the import mechanism in the `mmengine` configuration to build the part of the dataset parameters in the evaluation script, as shown below:
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```python
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from mmengine.config import read_base
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with read_base():
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from .datasets.CLUE_afqmc.CLUE_afqmc_gen_db509b import afqmc_datasets
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from .datasets.CLUE_cmnli.CLUE_cmnli_ppl_b78ad4 import cmnli_datasets
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datasets = []
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datasets += afqmc_datasets
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datasets += cmnli_datasets
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```
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Users can choose different abilities, different datasets and different evaluation methods configuration files to build the part of the dataset in the evaluation script according to their needs.
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For information on how to start an evaluation task and how to evaluate self-built datasets, please refer to the relevant documents.
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