84 lines
3.5 KiB
Markdown
84 lines
3.5 KiB
Markdown
# Configure Datasets
|
|
|
|
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.md#dataset-preparation).
|
|
|
|
## Directory Structure of Dataset Configuration Files
|
|
|
|
First, let's introduce the structure under the `configs/datasets` directory in OpenCompass, as shown below:
|
|
|
|
```
|
|
configs/datasets/
|
|
├── agieval
|
|
├── apps
|
|
├── ARC_c
|
|
├── ...
|
|
├── CLUE_afqmc # dataset
|
|
│ ├── CLUE_afqmc_gen_901306.py # different version of config
|
|
│ ├── CLUE_afqmc_gen.py
|
|
│ ├── CLUE_afqmc_ppl_378c5b.py
|
|
│ ├── CLUE_afqmc_ppl_6507d7.py
|
|
│ ├── CLUE_afqmc_ppl_7b0c1e.py
|
|
│ └── CLUE_afqmc_ppl.py
|
|
├── ...
|
|
├── XLSum
|
|
├── Xsum
|
|
└── z_bench
|
|
```
|
|
|
|
In the `configs/datasets` directory structure, we flatten all datasets directly, and there are multiple dataset configurations within the corresponding folders for each dataset.
|
|
|
|
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`.
|
|
|
|
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.
|
|
|
|
## Dataset Selection
|
|
|
|
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`.
|
|
|
|
```python
|
|
afqmc_datasets = [
|
|
dict(
|
|
abbr="afqmc-dev",
|
|
type=AFQMCDataset_V2,
|
|
path="./data/CLUE/AFQMC/dev.json",
|
|
reader_cfg=afqmc_reader_cfg,
|
|
infer_cfg=afqmc_infer_cfg,
|
|
eval_cfg=afqmc_eval_cfg,
|
|
),
|
|
]
|
|
```
|
|
|
|
And `cmnli_datasets` in `CLUE_cmnli/CLUE_cmnli_ppl_b78ad4.py`.
|
|
|
|
```python
|
|
cmnli_datasets = [
|
|
dict(
|
|
type=HFDataset,
|
|
abbr='cmnli',
|
|
path='json',
|
|
split='train',
|
|
data_files='./data/CLUE/cmnli/cmnli_public/dev.json',
|
|
reader_cfg=cmnli_reader_cfg,
|
|
infer_cfg=cmnli_infer_cfg,
|
|
eval_cfg=cmnli_eval_cfg)
|
|
]
|
|
```
|
|
|
|
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:
|
|
|
|
```python
|
|
from mmengine.config import read_base
|
|
|
|
with read_base():
|
|
from .datasets.CLUE_afqmc.CLUE_afqmc_gen_db509b import afqmc_datasets
|
|
from .datasets.CLUE_cmnli.CLUE_cmnli_ppl_b78ad4 import cmnli_datasets
|
|
|
|
datasets = []
|
|
datasets += afqmc_datasets
|
|
datasets += cmnli_datasets
|
|
```
|
|
|
|
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.
|
|
|
|
For information on how to start an evaluation task and how to evaluate self-built datasets, please refer to the relevant documents.
|