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[DOC] Add metric doc (#118)

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docs/en/index.rst
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@ -35,6 +35,7 @@ We always welcome *PRs* and *Issues* for the betterment of OpenCompass.
user_guides/models.md user_guides/models.md
user_guides/evaluation.md user_guides/evaluation.md
user_guides/experimentation.md user_guides/experimentation.md
user_guides/metrics.md
.. _AdvancedGuides: .. _AdvancedGuides:
.. toctree:: .. toctree::

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docs/en/user_guides/metrics.md
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# Metric Calculation # Metric Calculation
In the evaluation phase, we typically select the corresponding evaluation metric strategy based on the characteristics of the dataset itself. The main criterion is the **type of standard answer**, generally including the following types:
- **Choice**: Common in classification tasks, judgment questions, and multiple-choice questions. Currently, this type of question dataset occupies the largest proportion, with datasets such as MMLU, CEval, etc. Accuracy is usually used as the evaluation standard-- `ACCEvaluator`.
- **Phrase**: Common in Q&A and reading comprehension tasks. This type of dataset mainly includes CLUE_CMRC, CLUE_DRCD, DROP datasets, etc. Matching rate is usually used as the evaluation standard--`EMEvaluator`.
- **Sentence**: Common in translation and generating pseudocode/command-line tasks, mainly including Flores, Summscreen, Govrepcrs, Iwdlt2017 datasets, etc. BLEU (Bilingual Evaluation Understudy) is usually used as the evaluation standard--`BleuEvaluator`.
- **Paragraph**: Common in text summary generation tasks, commonly used datasets mainly include Lcsts, TruthfulQA, Xsum datasets, etc. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is usually used as the evaluation standard--`RougeEvaluator`.
- **Code**: Common in code generation tasks, commonly used datasets mainly include Humaneval, MBPP datasets, etc. Execution pass rate and `pass@k` are usually used as the evaluation standard. At present, Opencompass supports `MBPPEvaluator` and `HumanEvaluator`.
There is also a type of **scoring-type** evaluation task without standard answers, such as judging whether the output of a model is toxic, which can directly use the related API service for scoring. At present, it supports `ToxicEvaluator`, and currently, the realtoxicityprompts dataset uses this evaluation method.
## Supported Evaluation Metrics
Currently, in OpenCompass, commonly used Evaluators are mainly located in the [`opencompass/openicl/icl_evaluator`](https://github.com/InternLM/opencompass/tree/main/opencompass/openicl/icl_evaluator) folder. There are also some dataset-specific indicators that are placed in parts of [`opencompass/datasets`](https://github.com/InternLM/opencompass/tree/main/opencompass/datasets). Below is a summary:
| Evaluation Strategy | Evaluation Metrics | Common Postprocessing Method | Datasets |
| ------------------- | -------------------- | ---------------------------- | -------------------------------------------------------------------- |
| `ACCEvaluator` | Accuracy | `first_capital_postprocess` | agieval, ARC, bbh, mmlu, ceval, commonsenseqa, crowspairs, hellaswag |
| `EMEvaluator` | Match Rate | None, dataset-specific | drop, CLUE_CMRC, CLUE_DRCD |
| `BleuEvaluator` | BLEU | None, `flores` | flores, iwslt2017, summscreen, govrepcrs |
| `RougeEvaluator` | ROUGE | None, dataset-specific | lcsts, truthfulqa, Xsum, XLSum |
| `HumanEvaluator` | pass@k | `humaneval_postprocess` | humaneval_postprocess |
| `MBPPEvaluator` | Execution Pass Rate | None | mbpp |
| `ToxicEvaluator` | PerspectiveAPI | None | realtoxicityprompts |
| `AGIEvalEvaluator` | Accuracy | None | agieval |
| `AUCROCEvaluator` | AUC-ROC | None | jigsawmultilingual, civilcomments |
| `MATHEvaluator` | Accuracy | `math_postprocess` | math |
| `MccEvaluator` | Matthews Correlation | None | -- |
| `SquadEvaluator` | F1-scores | None | -- |
## How to Configure
The evaluation standard configuration is generally placed in the dataset configuration file, and the final xxdataset_eval_cfg will be passed to `dataset.infer_cfg` as an instantiation parameter.
Below is the definition of `govrepcrs_eval_cfg`, and you can refer to [configs/datasets/govrepcrs](https://github.com/InternLM/opencompass/tree/main/configs/datasets/govrepcrs).
```python
from opencompass.openicl.icl_evaluator import BleuEvaluator
from opencompass.datasets import GovRepcrsDataset
from opencompass.utils.text_postprocessors import general_cn_postprocess
govrepcrs_reader_cfg = dict(.......)
govrepcrs_infer_cfg = dict(.......)
# Configuration of evaluation metrics
govrepcrs_eval_cfg = dict(
evaluator=dict(type=BleuEvaluator), # Use the common translator evaluator BleuEvaluator
pred_role='BOT', # Accept 'BOT' role output
pred_postprocessor=dict(type=general_cn_postprocess), # Postprocessing of prediction results
dataset_postprocessor=dict(type=general_cn_postprocess)) # Postprocessing of dataset standard answers
govrepcrs_datasets = [
dict(
type=GovRepcrsDataset, # Dataset class name
path='./data/govrep/', # Dataset path
abbr='GovRepcrs', # Dataset alias
reader_cfg=govrepcrs_reader_cfg, # Dataset reading configuration file, configure its reading split, column, etc.
infer_cfg=govrepcrs_infer_cfg, # Dataset inference configuration file, mainly related to prompt
eval_cfg=govrepcrs_eval_cfg) # Dataset result evaluation configuration file, evaluation standard, and preprocessing and postprocessing.
]
```

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docs/zh_cn/index.rst
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@ -36,6 +36,7 @@ OpenCompass 上手路线
user_guides/models.md user_guides/models.md
user_guides/evaluation.md user_guides/evaluation.md
user_guides/experimentation.md user_guides/experimentation.md
user_guides/metrics.md
.. _提示词: .. _提示词:
.. toctree:: .. toctree::

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docs/zh_cn/user_guides/metrics.md
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# 评估指标 # 评估指标
Coming soon. 在评测阶段，我们一般以数据集本身的特性来选取对应的评估策略，最主要的依据为**标准答案的类型**，一般以下几种类型：
- **选项**：常见于分类任务，判断题以及选择题，目前这类问题的数据集占比最大，有 MMLU, CEval数据集等等，评估标准一般使用准确率--`ACCEvaluator`。
- **短语**：常见于问答以及阅读理解任务，这类数据集主要包括CLUE_CMRC, CLUE_DRCD, DROP数据集等等，评估标准一般使用匹配率--`EMEvaluator`。
- **句子**：常见于翻译以及生成伪代码、命令行任务中，主要包括Flores, Summscreen, Govrepcrs, Iwdlt2017数据集等等，评估标准一般使用BLEU(Bilingual Evaluation Understudy)--`BleuEvaluator`。
- **段落**：常见于文本摘要生成的任务，常用的数据集主要包括Lcsts, TruthfulQA, Xsum数据集等等，评估标准一般使用ROUGE（Recall-Oriented Understudy for Gisting Evaluation）--`RougeEvaluator`。
- **代码**：常见于代码生成的任务，常用的数据集主要包括Humaneval，MBPP数据集等等，评估标准一般使用执行通过率以及 `pass@k`，目前 Opencompass 支持的有`MBPPEvaluator`、`HumanEvaluator`。
还有一类**打分类型**评测任务没有标准答案，比如评判一个模型的输出是否存在有毒，可以直接使用相关 API 服务进行打分，目前支持的有 `ToxicEvaluator`，目前有 realtoxicityprompts 数据集使用此评测方式。
## 已支持评估指标
目前 OpenCompass 中，常用的 Evaluator 主要放在 [`opencompass/openicl/icl_evaluator`](https://github.com/InternLM/opencompass/tree/main/opencompass/openicl/icl_evaluator)文件夹下， 还有部分数据集特有指标的放在 [`opencompass/datasets`](https://github.com/InternLM/opencompass/tree/main/opencompass/datasets) 的部分文件中。以下是汇总：
| 评估指标 | 评估策略 | 常用后处理方式 | 数据集 |
| ------------------ | -------------------- | --------------------------- | -------------------------------------------------------------------- |
| `ACCEvaluator` | 正确率 | `first_capital_postprocess` | agieval, ARC, bbh, mmlu, ceval, commonsenseqa, crowspairs, hellaswag |
| `EMEvaluator` | 匹配率 | None, dataset_specification | drop, CLUE_CMRC, CLUE_DRCD |
| `BleuEvaluator` | BLEU | None, `flores` | flores, iwslt2017, summscreen, govrepcrs |
| `RougeEvaluator` | ROUGE | None, dataset_specification | lcsts, truthfulqa, Xsum, XLSum |
| `HumanEvaluator` | pass@k | `humaneval_postprocess` | humaneval_postprocess |
| `MBPPEvaluator` | 执行通过率 | None | mbpp |
| `ToxicEvaluator` | PerspectiveAPI | None | realtoxicityprompts |
| `AGIEvalEvaluator` | 正确率 | None | agieval |
| `AUCROCEvaluator` | AUC-ROC | None | jigsawmultilingual, civilcomments |
| `MATHEvaluator` | 正确率 | `math_postprocess` | math |
| `MccEvaluator` | Matthews Correlation | None | -- |
| `SquadEvaluator` | F1-scores | None | -- |
## 如何配置
评估标准配置一般放在数据集配置文件中，最终的 xxdataset_eval_cfg 会传给 `dataset.infer_cfg` 作为实例化的一个参数。
下面是 `govrepcrs_eval_cfg` 的定义， 具体可查看 [configs/datasets/govrepcrs](https://github.com/InternLM/opencompass/tree/main/configs/datasets/govrepcrs)。
```python
from opencompass.openicl.icl_evaluator import BleuEvaluator
from opencompass.datasets import GovRepcrsDataset
from opencompass.utils.text_postprocessors import general_cn_postprocess
govrepcrs_reader_cfg = dict(.......)
govrepcrs_infer_cfg = dict(.......)
# 评估指标的配置
govrepcrs_eval_cfg = dict(
evaluator=dict(type=BleuEvaluator), # 使用常用翻译的评估器BleuEvaluator
pred_role='BOT', # 接受'BOT' 角色的输出
pred_postprocessor=dict(type=general_cn_postprocess), # 预测结果的后处理
dataset_postprocessor=dict(type=general_cn_postprocess)) # 数据集标准答案的后处理
govrepcrs_datasets = [
dict(
type=GovRepcrsDataset, # 数据集类名
path='./data/govrep/', # 数据集路径
abbr='GovRepcrs', # 数据集别名
reader_cfg=govrepcrs_reader_cfg, # 数据集读取配置文件，配置其读取的split，列等
infer_cfg=govrepcrs_infer_cfg, # 数据集推理的配置文件，主要 prompt 相关
eval_cfg=govrepcrs_eval_cfg) # 数据集结果的评估配置文件，评估标准以及前后处理。
]
```