查看模型评估报告
更新时间：2025-01-14
功能介绍
用于获取评估报告（整体指标）。
使用说明
本文API支持通过Python SDK、Go SDK、Java SDK 和 Node.js SDK调用，调用流程请参考SDK安装及使用流程。
SDK调用
调用示例
import os
from qianfan import resources
# 通过环境变量初始化认证信息
# 使用安全认证AK/SK调用，替换下列示例中参数，安全认证Access Key替换your_iam_ak，Secret Key替换your_iam_sk，如何获取请查看https://cloud.baidu.com/doc/Reference/s/9jwvz2egb
os.environ["QIANFAN_ACCESS_KEY"] = "your_iam_ak"
os.environ["QIANFAN_SECRET_KEY"] = "your_iam_sk"
resp = resources.console.utils.call_action(
# 调用本文API，该参数值为固定值，无需修改；对应API调用文档-请求结构-请求地址的后缀
"/wenxinworkshop/modelrepo/eval/report", "",
# 请查看本文请求参数说明，根据实际使用选择参数；对应API调用文档-请求参数-Body参数
{
"id":"ame-vwgs2ybhyhfv"
}
)
print(resp.body)package main
import (
"context"
"fmt"
"os"
"github.com/baidubce/bce-qianfan-sdk/go/qianfan"
)
func main() {
// 使用安全认证AK/SK鉴权，通过环境变量初始化；替换下列示例中参数，安全认证Access Key替换your_iam_ak，Secret Key替换your_iam_sk
os.Setenv("QIANFAN_ACCESS_KEY", "your_iam_ak")
os.Setenv("QIANFAN_SECRET_KEY", "your_iam_sk")
ca := qianfan.NewConsoleAction()
res, err := ca.Call(context.TODO(),
// 调用本文API，该参数值为固定值，无需修改；对应API调用文档-请求结构-请求地址的后缀
"/wenxinworkshop/modelrepo/eval/report", "",
// 请查看本文请求参数说明，根据实际使用选择参数；对应API调用文档-请求参数-Body参数
map[string]interface{}{
"id":"ame-vwgs2ybhyhfv",
})
if err != nil {
panic(err)
}
fmt.Println(string(res.Body))
}import com.baidubce.qianfan.Qianfan;
import com.baidubce.qianfan.model.console.ConsoleResponse;
import com.baidubce.qianfan.util.CollUtils;
import com.baidubce.qianfan.util.Json;
import java.util.Map;
public class Dome {
public static void main(String args[]){
// 使用安全认证AK/SK鉴权，替换下列示例中参数，安全认证Access Key替换your_iam_ak，Secret Key替换your_iam_sk
Qianfan qianfan = new Qianfan("your_iam_ak", "your_iam_sk");
ConsoleResponse<List<Map<String, Object>>> response = qianfan.console()
// 调用本文API，该参数值为固定值，无需修改；对应API调用文档-请求结构-请求地址的后缀
.route("/wenxinworkshop/modelrepo/eval/report")
// 需要传入参数的场景，可以自行封装请求类，或者使用Map.of()来构建请求Body
// Java 8可以使用SDK提供的CollUtils.mapOf()来替代Map.of()
// 请查看本文请求参数说明，根据实际使用选择参数；对应API调用文档-请求参数-Body参数
.body(CollUtils.mapOf(
"id","ame-vwgs2ybhyhfv"
))
.execute(new TypeRef<List<Map<String, Object>>>() {});
System.out.println(Json.serialize(response));
}
}import {consoleAction, setEnvVariable} from "@baiducloud/qianfan";
// 使用安全认证AK/SK鉴权，通过环境变量初始化；替换下列示例中参数，安全认证Access Key替换your_iam_ak，Secret Key替换your_iam_sk
setEnvVariable('QIANFAN_ACCESS_KEY','your_iam_ak');
setEnvVariable('QIANFAN_SECRET_KEY','your_iam_sk');
async function main() {
//base_api_route:调用本文API，该参数值为固定值，无需修改；对应API调用文档-请求结构-请求地址的后缀
//data:请查看本文请求参数说明，根据实际使用选择参数；对应API调用文档-请求参数-Body参数
const res = await consoleAction({base_api_route: '/wenxinworkshop/modelrepo/eval/report', data: {
"id":"ame-vwgs2ybhyhfv"
}
});
console.log(res);
}
main();返回示例
{
"log_id": "3617826755",
"result": [
{
"modelName": "llama2_7b_32k_z_sft",
"modelVersion": "1",
"modelVersionSource": "Train",
"evalMode": "manual",
"evaluationName": "cl_联调_模型评估_用户bos",
"id": "65eae1fb1xxx9ca97a1",
"modelVersionId": 833,
"modelId": 591,
"userId": 1,
"evaluationId": 401,
"modelForm": "model",
"modelIdStr": "am-dkxwxxxxjgw",
"modelVersionIdStr": "amv-7ab3xxxtspe1",
"evaluationIdStr": "ame-28zxxx2rn4",
"evalUnitId": "ameu-gpvzxxxs0n",
"inferDatasetId": "ds-p79kyxxx7sbk",
"inferDatasetName": "cl_联调_模型评估_用户bos_llama2_7b_32k_z_sft_V1_jmrr",
"effectMetric": {
"accuracy": 0,
"f1Score": 0,
"rouge_1": 0,
"rouge_2": 0,
"rouge_l": 0,
"bleu4": 0,
"avgJudgeScore": 0,
"stdJudgeScore": 0,
"medianJudgeScore": 0,
"scoreDistribution": null,
"manualAvgScore": 0.5,
"goodCaseProportion": 0,
"subjectiveImpression": "1",
"manualScoreDistribution": [
{
"dimension": "满意度",
"scoreDistribution": {
"-1": 2,
"1": 1
}
},
{
"dimension": "安全性",
"scoreDistribution": {
"-1": 2,
"0": 1
}
}
]
},
"performanceMetric": {}
},
{
"modelName": "mixtral2",
"modelVersion": "8",
"modelVersionSource": "Train",
"evalMode": "manual",
"evaluationName": "cl_联调_模型评估_用户bos",
"id": "65eae45dxxxcab739",
"modelVersionId": 7xx,
"modelId": 545,
"userId": 1,
"evaluationId": 401,
"modelForm": "model",
"modelIdStr": "am-ktcxxx88z",
"modelVersionIdStr": "amv-g2acxxxg9v",
"evaluationIdStr": "ame-28zxxx2rn4",
"evalUnitId": "ameu-1uxpxxx8uc2",
"inferDatasetId": "ds-ba82xxxguh",
"inferDatasetName": "cl_联调_模型评估_用户bos_mixtral2_V8_x5xt",
"effectMetric": {
"accuracy": 0,
"f1Score": 0,
"rouge_1": 0,
"rouge_2": 0,
"rouge_l": 0,
"bleu4": 0,
"avgJudgeScore": 0,
"stdJudgeScore": 0,
"medianJudgeScore": 0,
"scoreDistribution": null,
"manualAvgScore": 0.5,
"goodCaseProportion": 0,
"subjectiveImpression": "2",
"manualScoreDistribution": [
{
"dimension": "满意度",
"scoreDistribution": {
"-1": 2,
"1": 1
}
},
{
"dimension": "安全性",
"scoreDistribution": {
"-1": 2,
"0": 1
}
}
]
},
"performanceMetric": {}
}
]
}{
"log_id": "3617826755",
"result": [
{
"modelName": "llama2_7b_32k_z_sft",
"modelVersion": "1",
"modelVersionSource": "Train",
"evalMode": "manual",
"evaluationName": "cl_联调_模型评估_用户bos",
"id": "65eae1fb1xxx9ca97a1",
"modelVersionId": 833,
"modelId": 591,
"userId": 1,
"evaluationId": 401,
"modelForm": "model",
"modelIdStr": "am-dkxwxxxxjgw",
"modelVersionIdStr": "amv-7ab3xxxtspe1",
"evaluationIdStr": "ame-28zxxx2rn4",
"evalUnitId": "ameu-gpvzxxxs0n",
"inferDatasetId": "ds-p79kyxxx7sbk",
"inferDatasetName": "cl_联调_模型评估_用户bos_llama2_7b_32k_z_sft_V1_jmrr",
"effectMetric": {
"accuracy": 0,
"f1Score": 0,
"rouge_1": 0,
"rouge_2": 0,
"rouge_l": 0,
"bleu4": 0,
"avgJudgeScore": 0,
"stdJudgeScore": 0,
"medianJudgeScore": 0,
"scoreDistribution": null,
"manualAvgScore": 0.5,
"goodCaseProportion": 0,
"subjectiveImpression": "1",
"manualScoreDistribution": [
{
"dimension": "满意度",
"scoreDistribution": {
"-1": 2,
"1": 1
}
},
{
"dimension": "安全性",
"scoreDistribution": {
"-1": 2,
"0": 1
}
}
]
},
"performanceMetric": {}
},
{
"modelName": "mixtral2",
"modelVersion": "8",
"modelVersionSource": "Train",
"evalMode": "manual",
"evaluationName": "cl_联调_模型评估_用户bos",
"id": "65eae45dxxxcab739",
"modelVersionId": 7xx,
"modelId": 545,
"userId": 1,
"evaluationId": 401,
"modelForm": "model",
"modelIdStr": "am-ktcxxx88z",
"modelVersionIdStr": "amv-g2acxxxg9v",
"evaluationIdStr": "ame-28zxxx2rn4",
"evalUnitId": "ameu-1uxpxxx8uc2",
"inferDatasetId": "ds-ba82xxxguh",
"inferDatasetName": "cl_联调_模型评估_用户bos_mixtral2_V8_x5xt",
"effectMetric": {
"accuracy": 0,
"f1Score": 0,
"rouge_1": 0,
"rouge_2": 0,
"rouge_l": 0,
"bleu4": 0,
"avgJudgeScore": 0,
"stdJudgeScore": 0,
"medianJudgeScore": 0,
"scoreDistribution": null,
"manualAvgScore": 0.5,
"goodCaseProportion": 0,
"subjectiveImpression": "2",
"manualScoreDistribution": [
{
"dimension": "满意度",
"scoreDistribution": {
"-1": 2,
"1": 1
}
},
{
"dimension": "安全性",
"scoreDistribution": {
"-1": 2,
"0": 1
}
}
]
},
"performanceMetric": {}
}
]
}{
"log_id": "3617826755",
"result": [
{
"modelName": "llama2_7b_32k_z_sft",
"modelVersion": "1",
"modelVersionSource": "Train",
"evalMode": "manual",
"evaluationName": "cl_联调_模型评估_用户bos",
"id": "65eae1fb1xxx9ca97a1",
"modelVersionId": 833,
"modelId": 591,
"userId": 1,
"evaluationId": 401,
"modelForm": "model",
"modelIdStr": "am-dkxwxxxxjgw",
"modelVersionIdStr": "amv-7ab3xxxtspe1",
"evaluationIdStr": "ame-28zxxx2rn4",
"evalUnitId": "ameu-gpvzxxxs0n",
"inferDatasetId": "ds-p79kyxxx7sbk",
"inferDatasetName": "cl_联调_模型评估_用户bos_llama2_7b_32k_z_sft_V1_jmrr",
"effectMetric": {
"accuracy": 0,
"f1Score": 0,
"rouge_1": 0,
"rouge_2": 0,
"rouge_l": 0,
"bleu4": 0,
"avgJudgeScore": 0,
"stdJudgeScore": 0,
"medianJudgeScore": 0,
"scoreDistribution": null,
"manualAvgScore": 0.5,
"goodCaseProportion": 0,
"subjectiveImpression": "1",
"manualScoreDistribution": [
{
"dimension": "满意度",
"scoreDistribution": {
"-1": 2,
"1": 1
}
},
{
"dimension": "安全性",
"scoreDistribution": {
"-1": 2,
"0": 1
}
}
]
},
"performanceMetric": {}
},
{
"modelName": "mixtral2",
"modelVersion": "8",
"modelVersionSource": "Train",
"evalMode": "manual",
"evaluationName": "cl_联调_模型评估_用户bos",
"id": "65eae45dxxxcab739",
"modelVersionId": 7xx,
"modelId": 545,
"userId": 1,
"evaluationId": 401,
"modelForm": "model",
"modelIdStr": "am-ktcxxx88z",
"modelVersionIdStr": "amv-g2acxxxg9v",
"evaluationIdStr": "ame-28zxxx2rn4",
"evalUnitId": "ameu-1uxpxxx8uc2",
"inferDatasetId": "ds-ba82xxxguh",
"inferDatasetName": "cl_联调_模型评估_用户bos_mixtral2_V8_x5xt",
"effectMetric": {
"accuracy": 0,
"f1Score": 0,
"rouge_1": 0,
"rouge_2": 0,
"rouge_l": 0,
"bleu4": 0,
"avgJudgeScore": 0,
"stdJudgeScore": 0,
"medianJudgeScore": 0,
"scoreDistribution": null,
"manualAvgScore": 0.5,
"goodCaseProportion": 0,
"subjectiveImpression": "2",
"manualScoreDistribution": [
{
"dimension": "满意度",
"scoreDistribution": {
"-1": 2,
"1": 1
}
},
{
"dimension": "安全性",
"scoreDistribution": {
"-1": 2,
"0": 1
}
}
]
},
"performanceMetric": {}
}
]
}{
log_id: '3617826755',
result: [
{
modelName: 'llama2_7b_32k_z_sft',
modelVersion: '1',
modelVersionSource: 'Train',
evalMode: 'manual',
evaluationName: 'cl_联调_模型评估_用户bos',
id: '65eae1fb1xxx9ca97a1',
modelVersionId: 833,
modelId: 591,
userId: 1,
evaluationId: 401,
modelForm: 'model',
modelIdStr: 'am-dkxwxxxxjgw',
modelVersionIdStr: 'amv-7ab3xxxtspe1',
evaluationIdStr: 'ame-28zxxx2rn4',
evalUnitId: 'ameu-gpvzxxxs0n',
inferDatasetId: 'ds-p79kyxxx7sbk',
inferDatasetName: 'cl_联调_模型评估_用户bos_llama2_7b_32k_z_sft_V1_jmrr',
effectMetric: {
accuracy: 0,
f1Score: 0,
rouge_1: 0,
rouge_2: 0,
rouge_l: 0,
bleu4: 0,
avgJudgeScore: 0,
stdJudgeScore: 0,
medianJudgeScore: 0,
scoreDistribution: null,
manualAvgScore: 0.5,
goodCaseProportion: 0,
subjectiveImpression: '1',
manualScoreDistribution: [
{
dimension: '满意度',
scoreDistribution: {
-1: 2,
1: 1
}
},
{
dimension: '安全性',
scoreDistribution: {
-1: 2,
0: 1
}
}
]
},
performanceMetric: {}
},
{
modelName: 'mixtral2',
modelVersion: '8',
modelVersionSource: 'Train',
evalMode: 'manual',
evaluationName: 'cl_联调_模型评估_用户bos',
id: '65eae45dxxxcab739',
modelVersionId: 7xx,
modelId: 545,
userId: 1,
evaluationId: 401,
modelForm: 'model',
modelIdStr: 'am-ktcxxx88z',
modelVersionIdStr: 'amv-g2acxxxg9v',
evaluationIdStr: 'ame-28zxxx2rn4',
evalUnitId: 'ameu-1uxpxxx8uc2',
inferDatasetId: 'ds-ba82xxxguh',
inferDatasetName: 'cl_联调_模型评估_用户bos_mixtral2_V8_x5xt',
effectMetric: {
accuracy: 0,
f1Score: 0,
rouge_1: 0,
rouge_2: 0,
rouge_l: 0,
bleu4: 0,
avgJudgeScore: 0,
stdJudgeScore: 0,
medianJudgeScore: 0,
scoreDistribution: null,
manualAvgScore: 0.5,
goodCaseProportion: 0,
subjectiveImpression: '2',
manualScoreDistribution: [
{
dimension: '满意度',
scoreDistribution: {
-1: 2,
1: 1
}
},
{
dimension: '安全性',
scoreDistribution: {
-1: 2,
0: 1
}
}
]
},
performanceMetric: {}
}
]
}请求参数
| 名称 | 类型 | 必填 | 描述 |
|---|---|---|---|
| id | string | 是 | 评估任务id，示例：ame-vwgs2ybhyhfv，说明： （1）可以通过以下方式获取该字段值： · 方式一，通过调用创建模型评估任务接口，返回的字段evalIdStr获取 · 方式二，在控制台-模型评估页面，点击某评估任务名称打开详情页，在任务详情的基本信息中查看，如下图所示  （2）该字段新增支持string类型，如果之前使用的是int类型，建议变更为string类型，后续可能将逐步废弃int类型；例如之前是通过调用创建模型评估任务接口，返回的字段evalId获取，建议替换为返回的evalIdStr获取 |
返回参数
| 名称 | 类型 | 描述 |
|---|---|---|
| log_id | string | 请求ID |
| result | object[] | 请求结果 |
result说明
| 名称 | 类型 | 描述 |
|---|---|---|
| evaluationId | int | 评估任务ID |
| evaluationName | string | 评估任务名称 |
| modelId | int | 模型ID |
| modelVersionId | int | 模型版本ID |
| modelName | string | 模型名 |
| modelVersion | string | 模型版本号 |
| modelVersionSource | string | 模型版本来源 |
| evalMode | string | 评估模式，说明： （1）有以下评估模式 ： · rule：基于规则 · model：裁判员模型 · manual：人工评估 （2）多个模式使用,拼接，示例“model,manual,rule” |
| effectMetric | object | 效果指标 |
| modelForm | string | 评估的物料类型，说明： · model：旧数据(推理结果集评估功能上线前的评估任务)类型都是模型，即值为model · inferDataset：推理结果集 |
| modelIdStr | string | 模型字符串id |
| modelVersionIdStr | string | 模型版本字符串id |
| evaluationIdStr | string | 评估任务字符串id |
| evalUnitId | string | 评估子任务id，用于唯一标识评估子任务 |
| inferDatasetId | string | 当前评估子任务使用的推理结果集id |
| inferDatasetName | string | 当前评估子任务使用的推理结果集名称 |
effectMetric说明
| 名称 | 类型 | 描述 |
|---|---|---|
| id | string | 单个评估报告的主键 |
| accuracy | number | 基于规则-准确率打分 |
| f1Score | number | 基于规则-准确率打分 |
| rouge_1 | number | 基于规则-相似度打分 |
| rouge_2 | number | 基于规则-相似度打分 |
| rouge_l | number | 基于规则-相似度打分 |
| bleu4 | number | 基于规则-相似度打分 |
| avgJudgeScore | number | 裁判员打分-均值 |
| stdJudgeScore | number | 裁判员打分-标准差 |
| medianJudgeScore | number | 裁判员打分-中位数 |
| scoreDistribution | map[string]int | 裁判员打分-分值分布，说明： · 含有从最小值到最大值的所有分数 · -1为无效打分 |
| manualAvgScore | number | 平均分 |
| goodCaseProportion | int | good case占比 |
| subjectiveImpression | string | 人工打分-主观印象 |
| manualScoreDistribution | object[] | 维度分数分布 |
manualScoreDistribution说明
| 名称 | 类型 | 描述 |
|---|---|---|
| dimension | string | 评价维度 |
| scoreDistribution | map[string]int | 维度分值分布，key为分值，value为分值的个数 |