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58 lines
2.6 KiB
Markdown
58 lines
2.6 KiB
Markdown
# Add a dataset
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Although OpenCompass has already included most commonly used datasets, users need to follow the steps below to support a new dataset if wanted:
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1. Add a dataset script `mydataset.py` to the `opencompass/datasets` folder. This script should include:
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- The dataset and its loading method. Define a `MyDataset` class that implements the data loading method `load` as a static method. This method should return data of type `datasets.Dataset`. We use the Hugging Face dataset as the unified interface for datasets to avoid introducing additional logic. Here's an example:
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```python
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import datasets
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from .base import BaseDataset
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class MyDataset(BaseDataset):
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@staticmethod
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def load(**kwargs) -> datasets.Dataset:
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pass
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```
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- (Optional) If the existing evaluators in OpenCompass do not meet your needs, you need to define a `MyDatasetEvaluator` class that implements the scoring method `score`. This method should take `predictions` and `references` as input and return the desired dictionary. Since a dataset may have multiple metrics, the method should return a dictionary containing the metrics and their corresponding scores. Here's an example:
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```python
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from opencompass.openicl.icl_evaluator import BaseEvaluator
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class MyDatasetEvaluator(BaseEvaluator):
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def score(self, predictions: List, references: List) -> dict:
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pass
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```
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- (Optional) If the existing postprocessors in OpenCompass do not meet your needs, you need to define the `mydataset_postprocess` method. This method takes an input string and returns the corresponding postprocessed result string. Here's an example:
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```python
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def mydataset_postprocess(text: str) -> str:
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pass
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```
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2. After defining the dataset loading, data postprocessing, and evaluator methods, you need to add the following configurations to the configuration file:
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```python
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from opencompass.datasets import MyDataset, MyDatasetEvaluator, mydataset_postprocess
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mydataset_eval_cfg = dict(
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evaluator=dict(type=MyDatasetEvaluator),
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pred_postprocessor=dict(type=mydataset_postprocess))
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mydataset_datasets = [
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dict(
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type=MyDataset,
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...,
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reader_cfg=...,
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infer_cfg=...,
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eval_cfg=mydataset_eval_cfg)
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]
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```
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Detailed dataset configuration files and other required configuration files can be referred to in the [Configuration Files](../user_guides/config.md) tutorial. For guides on launching tasks, please refer to the [Quick Start](../get_started/quick_start.md) tutorial.
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