Skip to content

GitLab

"csrc/vscode:/vscode.git/clone" did not exist on "0adaf7f90e1a367bc825a61242d4c33d30bb8586"
Commit be3dfa50 authored by jerrrrry's avatar jerrrrry
Browse files

Initial commit

parents
Pipeline #2876 failed with stages
in 0 seconds
Hide whitespace changes
Inline Side-by-side
Showing with 1630 additions and 0 deletions
opencompass/configs/datasets/bbh/bbh_gen_5b92b0.py 0 → 100644
View file @ be3dfa50
import os
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
bbh_datasets = []
for _name in bbh_multiple_choice_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(
evaluator=dict(type=BBHEvaluator_mcq),
pred_role='BOT',
pred_postprocessor=dict(type=bbh_mcq_postprocess),
dataset_postprocessor=dict(type=bbh_mcq_postprocess))
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
for _name in bbh_free_form_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
opencompass/configs/datasets/bbh/bbh_gen_5bf00b.py 0 → 100644
View file @ be3dfa50
import os
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
bbh_datasets = []
for _name in bbh_multiple_choice_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f'Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: '
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(
evaluator=dict(type=BBHEvaluator_mcq),
pred_role='BOT',
pred_postprocessor=dict(type=bbh_mcq_postprocess),
dataset_postprocessor=dict(type=bbh_mcq_postprocess))
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
for _name in bbh_free_form_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f'Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: '
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
opencompass/configs/datasets/bbh/bbh_gen_98fba6.py 0 → 100644
View file @ be3dfa50
import os
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
bbh_datasets = []
for _name in bbh_multiple_choice_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512, stopping_criteria=['Q:']))
bbh_eval_cfg = dict(
evaluator=dict(type=BBHEvaluator_mcq),
pred_role='BOT',
pred_postprocessor=dict(type=bbh_mcq_postprocess),
dataset_postprocessor=dict(type=bbh_mcq_postprocess))
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
for _name in bbh_free_form_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512, stopping_criteria=['Q:']))
bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
opencompass/configs/datasets/bbh/bbh_subset_settings.py 0 → 100644
View file @ be3dfa50
settings = [
('temporal_sequences', 'mcq'),
('disambiguation_qa', 'mcq'),
('date_understanding', 'mcq'),
('tracking_shuffled_objects_three_objects', 'mcq'),
('penguins_in_a_table', 'mcq'),
('geometric_shapes', 'mcq'),
('snarks', 'mcq'),
('ruin_names', 'mcq'),
('tracking_shuffled_objects_seven_objects', 'mcq'),
('tracking_shuffled_objects_five_objects', 'mcq'),
('logical_deduction_three_objects', 'mcq'),
('hyperbaton', 'mcq'),
('logical_deduction_five_objects', 'mcq'),
('logical_deduction_seven_objects', 'mcq'),
('movie_recommendation', 'mcq'),
('salient_translation_error_detection', 'mcq'),
('reasoning_about_colored_objects', 'mcq'),
('multistep_arithmetic_two', 'free_form'),
('navigate', 'free_form'),
('dyck_languages', 'free_form'),
('word_sorting', 'free_form'),
('sports_understanding', 'free_form'),
('boolean_expressions', 'free_form'),
('object_counting', 'free_form'),
('formal_fallacies', 'free_form'),
('causal_judgement', 'free_form'),
('web_of_lies', 'free_form'),
]
opencompass/configs/datasets/bigcodebench/bigcodebench_full_complete_gen.py 0 → 100644
View file @ be3dfa50
from mmengine.config import read_base
with read_base():
from .bigcodebench_full_complete_gen_faf748 import bigcodebench_full_complete_datasets # noqa: F401, F403
opencompass/configs/datasets/bigcodebench/bigcodebench_full_complete_gen_faf748.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (
BigCodeBenchDataset,
BigCodeBenchEvaluator
)
bigcodebench_full_reader_cfg = dict(
input_columns=['complete_prompt'],
output_column='test',
)
bigcodebench_full_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system',
fallback_role='HUMAN',
prompt='')],
round=[
dict(role='HUMAN', prompt='{complete_prompt}'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=1024)
)
bigcodebench_full_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='complete',
remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
dataset_version='full',
),
pred_role='BOT',
)
bigcodebench_full_complete_datasets = [
dict(
abbr='bigcodebench_full_complete',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_full_reader_cfg,
infer_cfg=bigcodebench_full_infer_cfg,
eval_cfg=bigcodebench_full_eval_cfg,
release_version='v0.1.2'
)
]
\ No newline at end of file
opencompass/configs/datasets/bigcodebench/bigcodebench_full_instruct_gen.py 0 → 100644
View file @ be3dfa50
from mmengine.config import read_base
with read_base():
from .bigcodebench_full_instruct_gen_8815eb import bigcodebench_full_instruct_datasets # noqa: F401, F403
opencompass/configs/datasets/bigcodebench/bigcodebench_full_instruct_gen_8815eb.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (
BigCodeBenchDataset,
BigCodeBenchEvaluator
)
bigcodebench_full_reader_cfg = dict(
input_columns=['instruct_prompt'],
output_column='test',
)
bigcodebench_full_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system',
fallback_role='HUMAN',
prompt='')],
round=[
dict(role='HUMAN', prompt='{instruct_prompt}'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=8192)
)
bigcodebench_full_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='instruct',
remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
dataset_version='full',
),
pred_role='BOT',
)
bigcodebench_full_instruct_datasets = [
dict(
abbr='bigcodebench_full_instruct',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_full_reader_cfg,
infer_cfg=bigcodebench_full_infer_cfg,
eval_cfg=bigcodebench_full_eval_cfg,
release_version='v0.1.2'
)
]
\ No newline at end of file
opencompass/configs/datasets/bigcodebench/bigcodebench_hard_complete_gen.py 0 → 100644
View file @ be3dfa50
from mmengine.config import read_base
with read_base():
from .bigcodebench_hard_complete_gen_faf748 import bigcodebench_hard_complete_datasets # noqa: F401, F403
opencompass/configs/datasets/bigcodebench/bigcodebench_hard_complete_gen_faf748.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (
BigCodeBenchDataset,
BigCodeBenchEvaluator
)
bigcodebench_hard_reader_cfg = dict(
input_columns=['complete_prompt'],
output_column='test',
)
bigcodebench_hard_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system',
fallback_role='HUMAN',
prompt='')],
round=[
dict(role='HUMAN', prompt='{complete_prompt}'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=1024)
)
bigcodebench_hard_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='complete',
remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
dataset_version='hard',
),
pred_role='BOT',
)
bigcodebench_hard_complete_datasets = [
dict(
abbr='bigcodebench_hard_complete',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_hard_reader_cfg,
infer_cfg=bigcodebench_hard_infer_cfg,
eval_cfg=bigcodebench_hard_eval_cfg,
release_version='v0.1.2',
dataset_version='hard',
)
]
\ No newline at end of file
opencompass/configs/datasets/bigcodebench/bigcodebench_hard_instruct_gen.py 0 → 100644
View file @ be3dfa50
from mmengine.config import read_base
with read_base():
from .bigcodebench_hard_instruct_gen_8815eb import bigcodebench_hard_instruct_datasets # noqa: F401, F403
opencompass/configs/datasets/bigcodebench/bigcodebench_hard_instruct_gen_8815eb.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (
BigCodeBenchDataset,
BigCodeBenchEvaluator
)
bigcodebench_hard_reader_cfg = dict(
input_columns=['instruct_prompt'],
output_column='test',
)
bigcodebench_hard_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system',
fallback_role='HUMAN',
prompt='')],
round=[
dict(role='HUMAN', prompt='{instruct_prompt}'),
]
)
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=8192)
)
bigcodebench_hard_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='instruct',
remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
dataset_version='hard',
),
pred_role='BOT',
)
bigcodebench_hard_instruct_datasets = [
dict(
abbr='bigcodebench_hard_instruct',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_hard_reader_cfg,
infer_cfg=bigcodebench_hard_infer_cfg,
eval_cfg=bigcodebench_hard_eval_cfg,
release_version='v0.1.2',
dataset_version='hard',
)
]
\ No newline at end of file
opencompass/configs/datasets/calm/README.md 0 → 100644
View file @ be3dfa50
# CaLM Lite
**CaLM Lite** is a lightweight version of CaLM.
**Ca**usal evaluation of **L**anguage **M**odels (CaLM), to the best of our knowledge, is the first comprehensive benchmark for evaluating the causal reasoning capabilities of language models. The CaLM framework establishes a foundational taxonomy consisting of four modules: causal target (i.e., what to evaluate), adaptation (i.e., how to obtain the results), metric (i.e., how to measure the results), and error (i.e., how to analyze the bad results).
<div align="center">
[🌐 Website](https://opencausalab.github.io/CaLM) |
[📃 Report](https://arxiv.org/abs/2405.00622) |[ 🎆 Github](https://github.com/OpenCausaLab/CaLM) | 📧 Welcome to join us by email at causalai@pjlab.org.cn
</div>
## Quick Start
### Data Preparation
Download dataset to data/ folder.
```
wget https://github.com/OpenCausaLab/CaLM/releases/download/v1.0.0.lite/calm.zip
unzip calm.zip
```
### Run Model and Infer
To obtain a concise output with only the average information for all tasks, use:
```
python run.py --models YOUR_MODEL --datasets calm --summarizer calm
```
If you want detailed information for each task, use:
```
python run.py --models YOUR_MODEL --datasets calm
```
The `--summarizer calm` flag in the first command is used to generate a summarized output, while omitting it in the second command will provide task-specific details.
## Available Causal Tasks
We provide 92 tasks for causal evaluation, stored in the `data/calm` folder. For more information about our causal tasks, refer to [tasks](https://github.com/OpenCausaLab/CaLM/blob/main/documents/tasks.md).
The directory structure is:
```
├── calm
| ├── association
| ├── causal_discovery # Rung of the causal ladder
| │ ├── abstract_reasoning # Causal scenario
| │ │ ├── AR-B_CaLM-AR_CN.json # Causal task
| │ | └── AR-B_CaLM-AR_EN.json # Causal task
| │ └── ...
| └── ...
└── ...
```
## Dataset
- **Dataset size**: CaLM Lite leverages a light dataset of **9200**, while CaLM uses a significantly larger dataset of 126,334. The table below details the English dataset composition, with the Chinese version structured identically.
- **Dataset configuration**: We prioritize balance in our dataset for **binary classification** and **choice selection** questions. By ensuring an equal number of each GT label, we minimize the risk of introducing bias into the model's testing. For **probability calculation**, CaLM-Lite takes extra attention to balance the number of problems across different causal reasoning processes. (For more details on how causal reasoning process is defined, please refer to Section 9.1.6 of the [paper](https://arxiv.org/abs/2405.00622).)
- **Efficient evaluation**: For enhanced evaluation efficiency, OpenCompass offers customizable methods. Refer to the [documentation](https://opencompass.org.cn/doc) for guidance on tailoring these methods to your needs.
| Causal ladder | Causal scenario | Subset | Question type | Mode | CaLM Lite | CaLM |
|---------------|-----------------|--------|---------------|------|-----------|------|
| Causal discovery | PCD | E-CARE | Binary classification | Natural | 100 | 2000 |
| Causal discovery | PCD | E-CARE | Choice selection | Natural | 100 | 1000 |
| Causal discovery | PCD | COPA | Binary classification | Natural | 100 | 2000 |
| Causal discovery | PCD | COPA | Choice selection | Natural | 100 | 1000 |
| Causal discovery | ECI | CTB | Binary classification | Natural | 100 | 596 |
| Causal discovery | ECI | ESC | Binary classification | Natural | 100 | 1000 |
| Causal discovery | ECI | MAVEN-ERE | Binary classification | Natural | 100 | 1000 |
| Causal discovery | AR | CaLM-AR | Binary classification | Symbolic | 100 | 1600 |
| Causal discovery | CA | FP | Binary classification | Symbolic | 100 | 1600 |
| Causal discovery | CA | FA | Binary classification | Symbolic | 100 | 1600 |
| Association | CORR | correlation | Binary classification | Natural | 100 | 1476 |
| Association | EAE | exp-away | Binary classification | Natural | 100 | 168 |
| Intervention | CB | collider-bias | Binary classification | Natural | 100 | 163 |
| Intervention | ATE | ATE-natural | Binary classification | Natural | 100 | 1600 |
| Intervention | ATE | ATE-basic | Probability calculation | Mathematical | 100 | 1600 |
| Intervention | ATE | ATE-hard | Probability calculation | Mathematical | 100 | 1600 |
| Intervention | CDE | CDE-natural | Binary classification | Natural | 100 | 1600 |
| Intervention | CDE | CDE-basic | Probability calculation | Mathematical | 100 | 1600 |
| Intervention | CDE | CDE-hard | Probability calculation | Mathematical | 100 | 1600 |
| Intervention | BAS | backadj | Binary classification | Natural | 100 | 227 |
| Intervention | BAS | max-BAS | Choice selection | Symbolic | 100 | 1600 |
| Intervention | BAS | min-BAS | Choice selection | Symbolic | 100 | 1600 |
| Intervention | BAS | mix-BAS | Choice selection | Symbolic | 100 | 1600 |
| Intervention | FAS | FAS | Choice selection | Symbolic | 100 | 1600 |
| Intervention | IV | CaLM-IV | Choice selection | Symbolic | 100 | 1600 |
| Intervention | CEI | 0.2-UC | Binary classification | Symbolic | 100 | 1600 |
| Intervention | CEI | 0.4-UC | Binary classification | Symbolic | 100 | 1600 |
| Intervention | CEI | 0.6-UC | Binary classification | Symbolic | 100 | 1600 |
| Intervention | CEI | 0.8-UC | Binary classification | Symbolic | 100 | 1600 |
| Counterfactuals | ETT | ETT-natural | Binary classification | Natural | 100 | 1600 |
| Counterfactuals | ETT | ETT-basic | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | ETT | ETT-hard | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | NDE | NDE-natural | Binary classification | Natural | 100 | 1600 |
| Counterfactuals | NDE | NDE-basic | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | NDE | NDE-hard | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | NIE | NIE-natural | Binary classification | Natural | 100 | 1600 |
| Counterfactuals | NIE | NIE-basic | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | NIE | NIE-hard | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | PN | PN-basic | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | PN | PN-hard | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | PS | PS-basic | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | PS | PS-hard | Probability calculation | Mathematical | 100 | 1600 |
| Counterfactuals | AC | causal judgement | Binary classification | Natural | 100 | 187 |
| Counterfactuals | CR | CRASS | Choice selection | Natural | 100 | 274 |
| Counterfactuals | CR | det-counterfactual | Binary classification | Natural | 100 | 1476 |
| Counterfactuals | CEG | E-CARE | Open-ended generation | Natural | 100 | 1000 |
| **Total** | | | | | 4600 | 63167 |
## Available Prompt Styles (Adaptation)
Basic Prompt is our default setting for efficient evaluation of CaLM Lite, but we provide flexibility for exploring additional prompts through CaLM. If you'd like to explore and compare a wider range of prompts, we encourage you to use CaLM. We provide a comprehensive and easy-to-follow guide to assist you in our [repository](https://github.com/OpenCausaLab/CaLM).
## Citation
```
@misc{chen2024causal,
title={Causal Evaluation of Language Models},
author={Sirui Chen and Bo Peng and Meiqi Chen and Ruiqi Wang and Mengying Xu and Xingyu Zeng and Rui Zhao and Shengjie Zhao and Yu Qiao and Chaochao Lu},
year={2024},
eprint={2405.00622},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
opencompass/configs/datasets/calm/calm.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import CaLMDataset, CaLMEvaluator
task_hiearchy_dict = {
# association/
# correlation/
'CORR-B_correlation_CN':'association/correlation/',
'CORR-B_correlation_EN':'association/correlation/',
# explaining_away_effect/
'EAE-B_exp-away_CN':'association/explaining_away_effect/',
'EAE-B_exp-away_EN':'association/explaining_away_effect/',
# causal_discovery/
# abstract_reasoning/
'AR-B_CaLM-AR_CN':'causal_discovery/abstract_reasoning/',
'AR-B_CaLM-AR_EN':'causal_discovery/abstract_reasoning/',
# causal_attribution/
'CA-B_FA_CN':'causal_discovery/causal_attribution/',
'CA-B_FA_EN':'causal_discovery/causal_attribution/',
'CA-B_FP_CN':'causal_discovery/causal_attribution/',
'CA-B_FP_EN':'causal_discovery/causal_attribution/',
# event_causality_identification/
'ECI-B_CTB_CN':'causal_discovery/event_causality_identification/',
'ECI-B_CTB_EN':'causal_discovery/event_causality_identification/',
'ECI-B_ESC_CN':'causal_discovery/event_causality_identification/',
'ECI-B_ESC_EN':'causal_discovery/event_causality_identification/',
'ECI-B_MAVEN-ERE_CN':'causal_discovery/event_causality_identification/',
'ECI-B_MAVEN-ERE_EN':'causal_discovery/event_causality_identification/',
# pairwise_causal_discovery/
'PCD-B_COPA_CN':'causal_discovery/pairwise_causal_discovery/',
'PCD-B_COPA_EN':'causal_discovery/pairwise_causal_discovery/',
'PCD-B_E-CARE_CN':'causal_discovery/pairwise_causal_discovery/',
'PCD-B_E-CARE_EN':'causal_discovery/pairwise_causal_discovery/',
'PCD-C_COPA_CN':'causal_discovery/pairwise_causal_discovery/',
'PCD-C_COPA_EN':'causal_discovery/pairwise_causal_discovery/',
'PCD-C_E-CARE_CN':'causal_discovery/pairwise_causal_discovery/',
'PCD-C_E-CARE_EN':'causal_discovery/pairwise_causal_discovery/',
# counterfactual/
# actual_causality/
'AC-B_causal_judgement_CN':'counterfactual/actual_causality/',
'AC-B_causal_judgement_EN':'counterfactual/actual_causality/',
# causal_explanation_generation/
'CEG-O_E-CARE_CN':'counterfactual/causal_explanation_generation/',
'CEG-O_E-CARE_EN':'counterfactual/causal_explanation_generation/',
# counterfactual_reasoning/
'CR-B_det-counterfactual_CN':'counterfactual/counterfactual_reasoning/',
'CR-B_det-counterfactual_EN':'counterfactual/counterfactual_reasoning/',
'CR-C_CRASS_CN':'counterfactual/counterfactual_reasoning/',
'CR-C_CRASS_EN':'counterfactual/counterfactual_reasoning/',
# effect_of_the_treatment_on_the_treated/
'ETT-B_ETT-natural_CN':'counterfactual/effect_of_the_treatment_on_the_treated/',
'ETT-B_ETT-natural_EN':'counterfactual/effect_of_the_treatment_on_the_treated/',
'ETT-P_ETT-basic_CN':'counterfactual/effect_of_the_treatment_on_the_treated/',
'ETT-P_ETT-basic_EN':'counterfactual/effect_of_the_treatment_on_the_treated/',
'ETT-P_ETT-hard_CN':'counterfactual/effect_of_the_treatment_on_the_treated/',
'ETT-P_ETT-hard_EN':'counterfactual/effect_of_the_treatment_on_the_treated/',
# natural_direct_effect/
'NDE-B_NDE-natural_CN':'counterfactual/natural_direct_effect/',
'NDE-B_NDE-natural_EN':'counterfactual/natural_direct_effect/',
'NDE-P_NDE-basic_CN':'counterfactual/natural_direct_effect/',
'NDE-P_NDE-basic_EN':'counterfactual/natural_direct_effect/',
'NDE-P_NDE-hard_CN':'counterfactual/natural_direct_effect/',
'NDE-P_NDE-hard_EN':'counterfactual/natural_direct_effect/',
# natural_indirect_effect/
'NIE-B_NIE-natural_CN':'counterfactual/natural_indirect_effect/',
'NIE-B_NIE-natural_EN':'counterfactual/natural_indirect_effect/',
'NIE-P_NIE-basic_CN':'counterfactual/natural_indirect_effect/',
'NIE-P_NIE-basic_EN':'counterfactual/natural_indirect_effect/',
'NIE-P_NIE-hard_CN':'counterfactual/natural_indirect_effect/',
'NIE-P_NIE-hard_EN':'counterfactual/natural_indirect_effect/',
# probability_of_necessity/
'PN-P_PN-basic_CN':'counterfactual/probability_of_necessity/',
'PN-P_PN-basic_EN':'counterfactual/probability_of_necessity/',
'PN-P_PN-hard_CN':'counterfactual/probability_of_necessity/',
'PN-P_PN-hard_EN':'counterfactual/probability_of_necessity/',
# probability_of_sufficiency/
'PS-P_PS-basic_CN':'counterfactual/probability_of_sufficiency/',
'PS-P_PS-basic_EN':'counterfactual/probability_of_sufficiency/',
'PS-P_PS-hard_CN':'counterfactual/probability_of_sufficiency/',
'PS-P_PS-hard_EN':'counterfactual/probability_of_sufficiency/',
# intervention/
# average_treatment_effect/
'ATE-B_ATE-natural_CN':'intervention/average_treatment_effect/',
'ATE-B_ATE-natural_EN':'intervention/average_treatment_effect/',
'ATE-P_ATE-basic_CN':'intervention/average_treatment_effect/',
'ATE-P_ATE-basic_EN':'intervention/average_treatment_effect/',
'ATE-P_ATE-hard_CN':'intervention/average_treatment_effect/',
'ATE-P_ATE-hard_EN':'intervention/average_treatment_effect/',
# backdoor_adjustment_set/
'BAS-B_backadj_CN':'intervention/backdoor_adjustment_set/',
'BAS-B_backadj_EN':'intervention/backdoor_adjustment_set/',
'BAS-C_max-BAS_CN':'intervention/backdoor_adjustment_set/',
'BAS-C_max-BAS_EN':'intervention/backdoor_adjustment_set/',
'BAS-C_min-BAS_CN':'intervention/backdoor_adjustment_set/',
'BAS-C_min-BAS_EN':'intervention/backdoor_adjustment_set/',
'BAS-C_mix-BAS_CN':'intervention/backdoor_adjustment_set/',
'BAS-C_mix-BAS_EN':'intervention/backdoor_adjustment_set/',
# causal_effect_identification/
'CEI-B_0.2-UC_CN':'intervention/causal_effect_identification/',
'CEI-B_0.2-UC_EN':'intervention/causal_effect_identification/',
'CEI-B_0.4-UC_CN':'intervention/causal_effect_identification/',
'CEI-B_0.4-UC_EN':'intervention/causal_effect_identification/',
'CEI-B_0.6-UC_CN':'intervention/causal_effect_identification/',
'CEI-B_0.6-UC_EN':'intervention/causal_effect_identification/',
'CEI-B_0.8-UC_CN':'intervention/causal_effect_identification/',
'CEI-B_0.8-UC_EN':'intervention/causal_effect_identification/',
# collider_bias/
'CB-B_collider-bias_CN':'intervention/collider_bias/',
'CB-B_collider-bias_EN':'intervention/collider_bias/',
# controlled_direct_effect/
'CDE-B_CDE-natural_CN':'intervention/controlled_direct_effect/',
'CDE-B_CDE-natural_EN':'intervention/controlled_direct_effect/',
'CDE-P_CDE-basic_CN':'intervention/controlled_direct_effect/',
'CDE-P_CDE-basic_EN':'intervention/controlled_direct_effect/',
'CDE-P_CDE-hard_CN':'intervention/controlled_direct_effect/',
'CDE-P_CDE-hard_EN':'intervention/controlled_direct_effect/',
# frontdoor_adjustment_set/
'FAS-C_FAS_CN':'intervention/frontdoor_adjustment_set/',
'FAS-C_FAS_EN':'intervention/frontdoor_adjustment_set/',
# instrumental_variable/
'IV-C_CaLM-IV_CN':'intervention/instrumental_variable/',
'IV-C_CaLM-IV_EN':'intervention/instrumental_variable/',}
calm_reader_cfg = dict(
input_columns=['question'],
output_column='gt_item')
calm_all_sets = list(set(key[:-3] for key in task_hiearchy_dict.keys()))
calm_datasets = []
for _name in calm_all_sets:
for _prompt_style in ['basic','basic-CN']:
_task_name = _name + ('_CN' if _prompt_style.endswith('-CN') else '_EN')
_path = f'./data/calm/{task_hiearchy_dict[_task_name]}{_task_name}.json'
calm_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template='{question}'),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=500))
calm_eval_cfg = dict(evaluator=dict(
type=CaLMEvaluator,
core_metrics=True,
error_analysis=True,
prompt_style=_prompt_style,
task=_task_name))
calm_datasets.append(
dict(
abbr=f'calm_{_task_name}',
type=CaLMDataset,
path=_path,
prompt_style=_prompt_style,
reader_cfg=calm_reader_cfg,
infer_cfg=calm_infer_cfg,
eval_cfg=calm_eval_cfg)
)
del _prompt_style, _task_name, _path, _name
opencompass/configs/datasets/ceval/README.md 0 → 100644
View file @ be3dfa50
# C-Eval
```bash
python3 run.py --models hf_internlm2_7b --datasets ceval_internal_ppl_93e5ce --debug
python3 run.py --models hf_internlm2_chat_7b --datasets ceval_internal_gen_2daf24 --debug
```
## Base Models
| model | ceval-test | ceval-test-hard | ceval-test-stem | ceval-test-social-science | ceval-test-humanities | ceval-test-other | ceval-dev | ceval-dev-hard | ceval-dev-stem | ceval-dev-social-science | ceval-dev-humanities | ceval-dev-other |
|:------------------------:|-------------:|------------------:|------------------:|----------------------------:|------------------------:|-------------------:|------------:|-----------------:|-----------------:|---------------------------:|-----------------------:|------------------:|
| llama-7b-turbomind | 26.61 | 27.75 | 27.20 | 26.31 | 25.90 | 26.52 | 27.44 | 27.68 | 27.16 | 29.49 | 24.18 | 29.36 |
| llama-13b-turbomind | 29.18 | 25.59 | 27.66 | 33.86 | 28.29 | 28.58 | 31.75 | 30.32 | 31.39 | 35.22 | 30.16 | 30.82 |
| llama-30b-turbomind | 35.09 | 31.68 | 34.56 | 39.89 | 33.02 | 33.76 | 37.70 | 31.97 | 34.80 | 42.72 | 41.19 | 34.93 |
| llama-65b-turbomind | 37.98 | 29.47 | 36.03 | 45.03 | 36.51 | 36.56 | 40.46 | 33.76 | 36.37 | 46.47 | 42.26 | 40.63 |
| llama-2-7b-turbomind | 30.13 | 26.26 | 29.29 | 33.02 | 31.02 | 28.15 | 32.70 | 25.85 | 28.75 | 39.75 | 37.04 | 29.13 |
| llama-2-13b-turbomind | 37.38 | 30.81 | 35.85 | 43.98 | 36.81 | 34.75 | 40.43 | 31.34 | 35.67 | 45.75 | 45.32 | 39.36 |
| llama-2-70b-turbomind | 49.53 | 33.48 | 44.73 | 60.19 | 50.93 | 47.17 | 50.26 | 32.53 | 44.83 | 59.44 | 54.45 | 47.58 |
| llama-3-8b-turbomind | 48.83 | 34.47 | 46.02 | 56.48 | 49.15 | 46.69 | 50.45 | 33.76 | 45.94 | 58.08 | 50.93 | 51.25 |
| llama-3-70b-turbomind | 66.56 | 54.09 | 64.08 | 76.43 | 64.38 | 64.25 | 67.30 | 52.35 | 62.67 | 77.89 | 69.76 | 63.65 |
| internlm2-1.8b-turbomind | 44.79 | 33.93 | 41.19 | 54.26 | 47.15 | 40.35 | 46.64 | 33.00 | 38.62 | 57.28 | 51.30 | 46.89 |
| internlm2-7b-turbomind | 63.54 | 45.32 | 58.10 | 76.40 | 66.94 | 58.32 | 64.23 | 40.09 | 54.37 | 76.88 | 70.11 | 64.77 |
| internlm2-20b-turbomind | 67.28 | 50.15 | 62.33 | 79.59 | 70.55 | 61.82 | 66.73 | 42.50 | 59.25 | 79.98 | 73.43 | 61.56 |
| qwen-1.8b-turbomind | 54.24 | 38.60 | 50.02 | 68.18 | 55.33 | 48.13 | 53.78 | 33.38 | 46.36 | 68.40 | 57.57 | 50.17 |
| qwen-7b-turbomind | 62.06 | 42.73 | 56.21 | 77.12 | 65.28 | 55.76 | 63.23 | 36.99 | 54.74 | 78.55 | 68.94 | 59.02 |
| qwen-14b-turbomind | 70.33 | 53.61 | 65.25 | 83.19 | 72.85 | 65.37 | 72.05 | 55.03 | 66.07 | 85.59 | 74.91 | 67.78 |
| qwen-72b-turbomind | 83.25 | 66.78 | 78.44 | 91.75 | 83.86 | 83.63 | 83.60 | 63.68 | 78.05 | 90.25 | 87.13 | 84.13 |
| qwen1.5-0.5b-hf | 48.36 | 35.55 | 44.72 | 62.00 | 48.51 | 42.41 | 50.43 | 37.00 | 46.28 | 62.64 | 48.11 | 49.18 |
| qwen1.5-1.8b-hf | 58.67 | 40.98 | 53.91 | 74.52 | 58.51 | 53.06 | 59.38 | 43.02 | 53.45 | 75.88 | 60.06 | 54.47 |
| qwen1.5-4b-hf | 66.55 | 48.50 | 61.45 | 81.12 | 67.90 | 61.22 | 66.46 | 43.12 | 56.76 | 82.89 | 67.61 | 68.03 |
| qwen1.5-7b-hf | 72.49 | 52.90 | 66.77 | 85.50 | 74.37 | 69.19 | 73.57 | 49.16 | 66.32 | 84.23 | 77.30 | 73.34 |
| qwen1.5-14b-hf | 76.93 | 60.50 | 72.08 | 88.81 | 77.95 | 73.94 | 77.86 | 54.81 | 71.55 | 86.79 | 82.86 | 76.23 |
| qwen1.5-32b-hf | 82.50 | 66.67 | 77.97 | 90.93 | 83.66 | 81.88 | 82.79 | 71.06 | 80.01 | 89.02 | 83.36 | 81.62 |
| qwen1.5-72b-hf | 83.03 | 65.09 | 77.90 | 91.47 | 83.85 | 83.86 | 83.72 | 64.09 | 77.26 | 91.87 | 87.64 | 84.14 |
| qwen1.5-moe-a2-7b-hf | 76.67 | 51.37 | 68.89 | 88.33 | 77.15 | 79.73 | 77.90 | 51.25 | 67.27 | 89.28 | 83.16 | 81.60 |
| mistral-7b-v0.1-hf | 43.76 | 33.85 | 42.23 | 49.97 | 41.10 | 43.54 | 47.54 | 33.97 | 44.74 | 54.80 | 51.52 | 42.06 |
| mistral-7b-v0.2-hf | 42.81 | 32.84 | 41.00 | 50.19 | 39.45 | 42.77 | 46.44 | 31.67 | 42.89 | 54.50 | 48.75 | 43.23 |
| mixtral-8x7b-v0.1-hf | 51.15 | 41.46 | 50.93 | 59.19 | 46.69 | 48.72 | 55.31 | 42.04 | 52.78 | 62.00 | 56.44 | 52.71 |
| mixtral-8x22b-v0.1-hf | 58.13 | 48.31 | 58.01 | 66.94 | 53.60 | 54.86 | 60.50 | 45.67 | 57.44 | 71.27 | 61.31 | 55.47 |
| yi-6b-hf | 70.78 | 43.72 | 60.54 | 83.29 | 75.39 | 73.40 | 73.13 | 46.87 | 63.14 | 85.52 | 78.70 | 74.45 |
| yi-34b-hf | 80.93 | 58.51 | 73.48 | 89.24 | 83.65 | 84.18 | 81.62 | 56.95 | 71.64 | 89.73 | 87.49 | 86.53 |
| deepseek-7b-base-hf | 43.68 | 28.90 | 37.03 | 53.55 | 50.14 | 40.34 | 45.07 | 31.94 | 38.81 | 56.68 | 47.10 | 43.85 |
| deepseek-67b-base-hf | 66.66 | 44.25 | 57.89 | 79.02 | 72.36 | 65.66 | 66.65 | 38.62 | 56.65 | 79.56 | 73.72 | 66.01 |
### Details on Test Split
| model | computer_network | operating_system | computer_architecture | college_programming | college_physics | college_chemistry | advanced_mathematics | probability_and_statistics | discrete_mathematics | electrical_engineer | metrology_engineer | high_school_mathematics |
|:------------------------:|-------------------:|-------------------:|------------------------:|----------------------:|------------------:|--------------------:|-----------------------:|-----------------------------:|-----------------------:|----------------------:|---------------------:|--------------------------:|
| llama-7b-turbomind | 29.82 | 25.70 | 26.94 | 30.99 | 32.95 | 23.66 | 26.01 | 22.89 | 27.45 | 30.09 | 26.48 | 33.13 |
| llama-13b-turbomind | 33.33 | 37.99 | 31.09 | 29.82 | 22.16 | 27.23 | 31.79 | 27.11 | 24.84 | 28.02 | 33.33 | 30.72 |
| llama-30b-turbomind | 40.94 | 48.60 | 40.41 | 34.21 | 32.95 | 35.71 | 36.42 | 32.53 | 27.45 | 31.56 | 36.07 | 30.12 |
| llama-65b-turbomind | 41.52 | 50.84 | 44.04 | 40.94 | 27.84 | 29.46 | 28.32 | 30.72 | 29.41 | 35.10 | 42.47 | 30.12 |
| llama-2-7b-turbomind | 33.92 | 37.99 | 34.72 | 30.99 | 26.70 | 21.88 | 31.79 | 25.30 | 24.18 | 31.56 | 39.73 | 30.12 |
| llama-2-13b-turbomind | 40.94 | 46.93 | 37.82 | 36.26 | 30.68 | 29.46 | 35.84 | 30.72 | 24.84 | 32.74 | 42.92 | 34.94 |
| llama-2-70b-turbomind | 55.56 | 58.66 | 53.89 | 47.95 | 34.09 | 33.48 | 32.95 | 27.11 | 34.64 | 37.76 | 57.99 | 29.52 |
| llama-3-8b-turbomind | 55.56 | 58.66 | 55.96 | 51.17 | 27.27 | 35.27 | 36.42 | 31.33 | 34.64 | 40.12 | 50.68 | 30.72 |
| llama-3-70b-turbomind | 69.59 | 75.98 | 69.95 | 71.64 | 49.43 | 58.04 | 52.02 | 53.01 | 58.82 | 45.72 | 68.95 | 40.96 |
| internlm2-1.8b-turbomind | 40.35 | 40.78 | 39.38 | 32.16 | 34.66 | 34.38 | 31.21 | 31.33 | 35.95 | 35.10 | 51.60 | 27.71 |
| internlm2-7b-turbomind | 56.14 | 57.54 | 62.69 | 49.42 | 43.75 | 48.21 | 34.68 | 32.53 | 33.33 | 41.00 | 60.27 | 40.36 |
| internlm2-20b-turbomind | 62.57 | 65.36 | 66.84 | 58.77 | 43.18 | 51.79 | 39.31 | 40.36 | 35.95 | 42.77 | 66.67 | 47.59 |
| qwen-1.8b-turbomind | 46.20 | 41.90 | 46.63 | 36.84 | 40.34 | 36.61 | 27.75 | 28.92 | 32.68 | 36.58 | 57.08 | 30.12 |
| qwen-7b-turbomind | 52.63 | 54.75 | 54.40 | 46.20 | 35.80 | 44.20 | 36.99 | 27.71 | 26.80 | 38.35 | 57.99 | 33.13 |
| qwen-14b-turbomind | 58.48 | 64.80 | 59.07 | 54.68 | 45.45 | 57.59 | 45.09 | 33.73 | 39.22 | 49.26 | 67.58 | 45.78 |
| qwen-72b-turbomind | 83.04 | 73.74 | 79.27 | 76.61 | 75.00 | 64.29 | 49.13 | 44.58 | 46.41 | 66.37 | 85.84 | 68.07 |
| qwen1.5-0.5b-hf | 37.43 | 40.22 | 41.45 | 35.09 | 40.91 | 34.82 | 30.06 | 27.11 | 26.80 | 29.79 | 54.34 | 31.93 |
| qwen1.5-1.8b-hf | 47.37 | 50.84 | 47.67 | 38.30 | 43.18 | 35.27 | 29.48 | 30.12 | 33.99 | 39.53 | 58.90 | 28.92 |
| qwen1.5-4b-hf | 62.57 | 56.98 | 56.99 | 46.78 | 48.30 | 45.98 | 40.46 | 34.34 | 31.37 | 46.61 | 62.10 | 43.37 |
| qwen1.5-7b-hf | 66.08 | 62.57 | 66.32 | 55.56 | 54.55 | 47.77 | 41.62 | 31.93 | 35.95 | 49.85 | 74.43 | 49.40 |
| qwen1.5-14b-hf | 71.35 | 66.48 | 68.39 | 64.91 | 57.95 | 65.62 | 41.62 | 40.36 | 47.71 | 56.64 | 79.45 | 56.63 |
| qwen1.5-32b-hf | 84.80 | 73.18 | 74.61 | 70.18 | 71.59 | 61.61 | 49.13 | 45.78 | 49.02 | 61.95 | 87.67 | 72.89 |
| qwen1.5-72b-hf | 85.38 | 73.74 | 78.24 | 78.36 | 72.73 | 63.39 | 43.35 | 40.96 | 49.02 | 65.78 | 85.84 | 66.27 |
| qwen1.5-moe-a2-7b-hf | 77.78 | 73.74 | 68.91 | 64.91 | 66.48 | 49.11 | 33.53 | 36.75 | 35.95 | 61.06 | 91.32 | 40.96 |
| mistral-7b-v0.1-hf | 55.56 | 55.31 | 56.99 | 48.25 | 39.77 | 39.29 | 33.53 | 25.90 | 31.37 | 35.99 | 45.21 | 27.11 |
| mistral-7b-v0.2-hf | 56.14 | 53.63 | 55.44 | 47.66 | 36.36 | 34.38 | 32.37 | 25.30 | 33.33 | 31.86 | 45.21 | 29.52 |
| mixtral-8x7b-v0.1-hf | 62.57 | 64.80 | 60.10 | 60.53 | 38.64 | 42.41 | 40.46 | 37.35 | 45.75 | 35.99 | 60.27 | 34.94 |
| mixtral-8x22b-v0.1-hf | 65.50 | 74.86 | 63.73 | 65.79 | 46.59 | 52.68 | 52.02 | 45.78 | 52.94 | 42.77 | 62.56 | 39.16 |
| yi-6b-hf | 68.42 | 63.13 | 69.43 | 57.89 | 42.05 | 48.66 | 31.79 | 33.13 | 28.76 | 49.85 | 74.89 | 37.35 |
| yi-34b-hf | 83.63 | 80.45 | 74.09 | 68.42 | 62.50 | 60.27 | 45.09 | 38.55 | 50.33 | 65.19 | 88.58 | 49.40 |
| deepseek-7b-base-hf | 44.44 | 44.13 | 44.56 | 36.26 | 30.68 | 29.02 | 32.37 | 24.70 | 26.14 | 35.99 | 48.86 | 28.31 |
| deepseek-67b-base-hf | 63.16 | 70.39 | 65.80 | 59.36 | 42.61 | 45.54 | 35.84 | 38.55 | 42.48 | 44.54 | 68.95 | 33.73 |
| model | high_school_physics | high_school_chemistry | high_school_biology | middle_school_mathematics | middle_school_biology | middle_school_physics | middle_school_chemistry | veterinary_medicine | college_economics | business_administration | marxism | mao_zedong_thought |
|:------------------------:|----------------------:|------------------------:|----------------------:|----------------------------:|------------------------:|------------------------:|--------------------------:|----------------------:|--------------------:|--------------------------:|----------:|---------------------:|
| llama-7b-turbomind | 29.14 | 26.74 | 24.57 | 29.94 | 22.92 | 23.60 | 20.00 | 30.95 | 29.98 | 24.58 | 25.70 | 25.11 |
| llama-13b-turbomind | 22.29 | 18.60 | 28.00 | 26.55 | 26.56 | 25.28 | 19.46 | 29.05 | 28.77 | 28.57 | 39.66 | 43.38 |
| llama-30b-turbomind | 25.14 | 33.14 | 36.00 | 31.07 | 39.06 | 28.09 | 33.51 | 38.10 | 35.21 | 35.88 | 48.04 | 33.33 |
| llama-65b-turbomind | 33.71 | 26.16 | 38.29 | 33.90 | 44.27 | 36.52 | 38.92 | 38.10 | 37.42 | 42.19 | 59.22 | 48.40 |
| llama-2-7b-turbomind | 26.86 | 23.26 | 26.86 | 28.81 | 28.12 | 29.78 | 22.70 | 30.48 | 31.79 | 30.56 | 33.52 | 36.07 |
| llama-2-13b-turbomind | 28.00 | 31.98 | 36.57 | 36.72 | 38.54 | 36.52 | 37.84 | 46.67 | 37.02 | 36.54 | 57.54 | 41.10 |
| llama-2-70b-turbomind | 40.00 | 36.05 | 48.00 | 36.72 | 66.67 | 55.06 | 55.68 | 52.86 | 51.91 | 48.50 | 68.16 | 60.73 |
| llama-3-8b-turbomind | 41.71 | 38.37 | 50.86 | 36.16 | 61.98 | 63.48 | 63.78 | 56.19 | 41.65 | 49.17 | 69.27 | 54.34 |
| llama-3-70b-turbomind | 63.43 | 56.98 | 69.14 | 59.32 | 84.90 | 75.28 | 78.92 | 79.52 | 68.81 | 59.80 | 86.59 | 79.91 |
| internlm2-1.8b-turbomind | 30.29 | 45.93 | 46.29 | 33.33 | 63.02 | 60.11 | 62.70 | 47.62 | 35.61 | 37.87 | 69.27 | 61.64 |
| internlm2-7b-turbomind | 64.57 | 65.12 | 76.00 | 54.80 | 91.15 | 85.96 | 90.27 | 74.29 | 57.34 | 50.50 | 86.59 | 83.56 |
| internlm2-20b-turbomind | 68.57 | 74.42 | 78.86 | 58.76 | 91.67 | 90.45 | 90.27 | 72.38 | 57.95 | 55.81 | 88.83 | 88.58 |
| qwen-1.8b-turbomind | 55.43 | 56.98 | 61.14 | 54.80 | 85.42 | 84.83 | 85.41 | 54.76 | 43.06 | 44.19 | 83.80 | 79.91 |
| qwen-7b-turbomind | 68.00 | 69.19 | 82.86 | 57.63 | 93.75 | 87.64 | 92.43 | 63.81 | 47.28 | 57.48 | 86.59 | 82.65 |
| qwen-14b-turbomind | 78.86 | 83.14 | 92.57 | 67.23 | 96.88 | 95.51 | 96.76 | 73.33 | 56.94 | 64.45 | 91.62 | 86.76 |
| qwen-72b-turbomind | 93.14 | 93.60 | 95.43 | 88.70 | 98.44 | 97.75 | 99.46 | 90.00 | 75.45 | 80.73 | 96.09 | 99.54 |
| qwen1.5-0.5b-hf | 48.57 | 44.19 | 60.00 | 40.68 | 73.44 | 69.66 | 78.92 | 49.05 | 34.41 | 40.20 | 79.89 | 74.43 |
| qwen1.5-1.8b-hf | 58.86 | 68.02 | 76.00 | 59.32 | 91.15 | 90.45 | 87.03 | 63.81 | 44.87 | 48.50 | 86.03 | 90.41 |
| qwen1.5-4b-hf | 66.86 | 77.33 | 82.86 | 68.93 | 95.31 | 92.70 | 97.30 | 71.90 | 51.31 | 61.13 | 91.62 | 94.52 |
| qwen1.5-7b-hf | 79.43 | 82.56 | 91.43 | 77.40 | 96.88 | 95.51 | 96.22 | 80.00 | 62.37 | 69.77 | 93.30 | 97.26 |
| qwen1.5-14b-hf | 86.29 | 87.79 | 93.14 | 83.05 | 97.92 | 95.51 | 97.84 | 82.86 | 63.78 | 77.08 | 95.53 | 96.35 |
| qwen1.5-32b-hf | 88.00 | 95.35 | 94.86 | 91.53 | 97.92 | 99.44 | 100.00 | 90.00 | 73.44 | 78.74 | 94.97 | 98.63 |
| qwen1.5-72b-hf | 91.43 | 93.60 | 95.43 | 88.70 | 97.92 | 98.31 | 99.46 | 90.00 | 74.25 | 80.40 | 94.41 | 98.63 |
| qwen1.5-moe-a2-7b-hf | 70.86 | 77.33 | 82.86 | 68.36 | 97.92 | 93.26 | 97.30 | 89.52 | 70.22 | 74.75 | 96.09 | 98.17 |
| mistral-7b-v0.1-hf | 33.14 | 40.70 | 40.57 | 40.11 | 47.92 | 49.44 | 50.81 | 47.62 | 44.87 | 37.87 | 58.10 | 48.40 |
| mistral-7b-v0.2-hf | 34.86 | 36.63 | 45.71 | 36.72 | 46.35 | 46.07 | 48.65 | 43.81 | 43.46 | 39.53 | 57.54 | 48.86 |
| mixtral-8x7b-v0.1-hf | 49.71 | 42.44 | 53.71 | 47.46 | 62.50 | 61.24 | 60.00 | 57.62 | 52.52 | 44.52 | 68.72 | 57.99 |
| mixtral-8x22b-v0.1-hf | 54.29 | 43.02 | 58.29 | 55.93 | 76.04 | 66.29 | 75.68 | 66.19 | 60.97 | 51.83 | 74.30 | 70.78 |
| yi-6b-hf | 58.86 | 69.19 | 78.29 | 43.50 | 92.19 | 89.33 | 90.27 | 83.81 | 59.56 | 70.10 | 93.85 | 97.72 |
| yi-34b-hf | 80.00 | 81.98 | 93.14 | 65.54 | 97.40 | 95.51 | 96.76 | 92.86 | 74.04 | 76.08 | 94.97 | 97.26 |
| deepseek-7b-base-hf | 29.14 | 30.81 | 33.14 | 24.29 | 53.12 | 45.51 | 48.65 | 50.48 | 38.23 | 44.19 | 62.01 | 65.30 |
| deepseek-67b-base-hf | 60.00 | 55.23 | 64.00 | 46.33 | 84.90 | 79.78 | 83.24 | 73.33 | 57.75 | 63.79 | 89.94 | 88.58 |
| model | education_science | teacher_qualification | high_school_politics | high_school_geography | middle_school_politics | middle_school_geography | modern_chinese_history | ideological_and_moral_cultivation | logic | law | chinese_language_and_literature | art_studies |
|:------------------------:|--------------------:|------------------------:|-----------------------:|------------------------:|-------------------------:|--------------------------:|-------------------------:|------------------------------------:|--------:|------:|----------------------------------:|--------------:|
| llama-7b-turbomind | 22.96 | 31.58 | 25.57 | 29.78 | 22.80 | 25.00 | 21.70 | 21.51 | 25.00 | 26.24 | 22.49 | 25.84 |
| llama-13b-turbomind | 29.26 | 30.83 | 33.52 | 36.52 | 34.72 | 33.33 | 24.06 | 40.12 | 26.47 | 33.48 | 30.14 | 29.87 |
| llama-30b-turbomind | 37.41 | 46.37 | 32.95 | 38.20 | 50.78 | 40.74 | 28.77 | 45.93 | 33.33 | 32.13 | 39.23 | 22.82 |
| llama-65b-turbomind | 39.63 | 51.13 | 31.82 | 39.89 | 58.03 | 42.59 | 34.91 | 55.23 | 39.71 | 30.32 | 37.80 | 32.89 |
| llama-2-7b-turbomind | 27.78 | 34.34 | 31.82 | 34.83 | 35.23 | 34.26 | 28.77 | 38.95 | 32.35 | 33.94 | 27.27 | 30.87 |
| llama-2-13b-turbomind | 41.48 | 47.37 | 37.50 | 37.64 | 50.78 | 52.78 | 43.40 | 48.84 | 32.35 | 38.46 | 36.36 | 30.20 |
| llama-2-70b-turbomind | 57.78 | 69.17 | 50.57 | 58.43 | 69.95 | 66.67 | 50.94 | 72.09 | 50.98 | 42.53 | 44.98 | 52.01 |
| llama-3-8b-turbomind | 56.30 | 65.41 | 47.16 | 56.18 | 64.25 | 61.11 | 55.66 | 67.44 | 41.67 | 40.27 | 45.45 | 50.34 |
| llama-3-70b-turbomind | 72.22 | 85.46 | 75.00 | 74.72 | 84.97 | 76.85 | 75.00 | 76.16 | 59.31 | 52.94 | 62.68 | 68.46 |
| internlm2-1.8b-turbomind | 47.41 | 61.40 | 55.11 | 47.75 | 61.66 | 64.81 | 61.79 | 63.95 | 32.35 | 32.58 | 48.33 | 36.58 |
| internlm2-7b-turbomind | 66.67 | 85.96 | 78.98 | 74.72 | 91.71 | 87.96 | 80.66 | 80.23 | 42.16 | 50.23 | 64.11 | 70.13 |
| internlm2-20b-turbomind | 69.26 | 89.22 | 83.52 | 80.34 | 90.67 | 91.67 | 83.02 | 85.47 | 49.02 | 54.30 | 72.25 | 73.15 |
| qwen-1.8b-turbomind | 51.11 | 70.68 | 71.02 | 62.36 | 88.60 | 87.04 | 69.81 | 73.26 | 29.90 | 46.15 | 50.24 | 47.32 |
| qwen-7b-turbomind | 57.41 | 83.71 | 88.64 | 79.78 | 93.26 | 94.44 | 75.47 | 79.07 | 42.16 | 47.96 | 59.33 | 65.10 |
| qwen-14b-turbomind | 72.96 | 89.97 | 93.75 | 83.71 | 96.37 | 95.37 | 86.32 | 87.21 | 50.00 | 60.63 | 66.99 | 72.48 |
| qwen-72b-turbomind | 85.56 | 96.24 | 95.45 | 93.26 | 97.93 | 97.22 | 92.45 | 91.86 | 67.65 | 76.92 | 75.12 | 83.89 |
| qwen1.5-0.5b-hf | 43.33 | 63.16 | 65.91 | 56.18 | 82.90 | 79.63 | 68.87 | 70.35 | 28.43 | 37.56 | 39.23 | 32.21 |
| qwen1.5-1.8b-hf | 57.41 | 76.44 | 81.25 | 75.84 | 92.75 | 91.67 | 79.72 | 81.98 | 34.31 | 47.96 | 47.85 | 43.62 |
| qwen1.5-4b-hf | 65.93 | 87.47 | 86.93 | 82.58 | 94.30 | 95.37 | 84.91 | 84.30 | 40.20 | 62.90 | 58.85 | 58.72 |
| qwen1.5-7b-hf | 69.26 | 91.98 | 90.91 | 89.89 | 95.85 | 94.44 | 89.15 | 87.21 | 48.04 | 67.87 | 63.16 | 68.12 |
| qwen1.5-14b-hf | 78.89 | 94.99 | 94.89 | 91.57 | 96.89 | 98.15 | 91.04 | 88.37 | 57.84 | 69.68 | 66.99 | 73.83 |
| qwen1.5-32b-hf | 83.70 | 95.99 | 93.75 | 94.38 | 98.45 | 97.22 | 90.57 | 91.28 | 70.10 | 76.92 | 76.56 | 80.87 |
| qwen1.5-72b-hf | 84.44 | 96.49 | 96.59 | 93.82 | 98.45 | 97.22 | 92.92 | 91.28 | 66.67 | 76.92 | 74.16 | 85.23 |
| qwen1.5-moe-a2-7b-hf | 80.74 | 95.49 | 89.20 | 89.33 | 94.82 | 94.44 | 92.45 | 91.28 | 52.45 | 75.57 | 67.94 | 79.87 |
| mistral-7b-v0.1-hf | 45.19 | 59.15 | 43.75 | 49.44 | 56.48 | 56.48 | 45.28 | 58.14 | 37.75 | 38.91 | 40.67 | 34.56 |
| mistral-7b-v0.2-hf | 45.93 | 58.65 | 38.07 | 48.31 | 63.21 | 58.33 | 41.98 | 54.07 | 35.78 | 40.27 | 38.28 | 32.21 |
| mixtral-8x7b-v0.1-hf | 57.04 | 67.92 | 53.41 | 55.06 | 69.95 | 64.81 | 47.64 | 70.93 | 42.16 | 38.01 | 46.41 | 36.58 |
| mixtral-8x22b-v0.1-hf | 60.37 | 72.68 | 64.77 | 65.17 | 77.20 | 71.30 | 57.08 | 75.00 | 49.51 | 43.44 | 52.63 | 49.33 |
| yi-6b-hf | 79.26 | 92.48 | 77.27 | 76.40 | 92.75 | 93.52 | 89.15 | 90.12 | 60.78 | 74.66 | 61.24 | 74.16 |
| yi-34b-hf | 84.81 | 96.24 | 88.07 | 88.20 | 96.37 | 96.30 | 91.98 | 91.28 | 75.00 | 78.73 | 80.38 | 82.89 |
| deepseek-7b-base-hf | 52.22 | 70.18 | 47.16 | 51.12 | 60.62 | 44.44 | 58.49 | 66.86 | 31.86 | 37.56 | 53.11 | 61.07 |
| deepseek-67b-base-hf | 76.67 | 89.22 | 77.27 | 78.65 | 89.64 | 78.70 | 85.85 | 84.30 | 50.00 | 64.25 | 69.38 | 84.23 |
| model | professional_tour_guide | legal_professional | high_school_chinese | high_school_history | middle_school_history | civil_servant | sports_science | plant_protection | basic_medicine | clinical_medicine | urban_and_rural_planner | accountant |
|:------------------------:|--------------------------:|---------------------:|----------------------:|----------------------:|------------------------:|----------------:|-----------------:|-------------------:|-----------------:|--------------------:|--------------------------:|-------------:|
| llama-7b-turbomind | 29.70 | 23.72 | 27.53 | 30.22 | 30.92 | 27.04 | 22.78 | 28.64 | 28.00 | 25.00 | 26.32 | 29.80 |
| llama-13b-turbomind | 25.94 | 20.93 | 25.84 | 29.67 | 24.64 | 29.60 | 26.67 | 29.15 | 33.71 | 25.50 | 28.47 | 28.44 |
| llama-30b-turbomind | 29.32 | 27.91 | 30.34 | 36.26 | 37.20 | 36.13 | 36.11 | 38.69 | 34.29 | 29.50 | 38.52 | 29.35 |
| llama-65b-turbomind | 28.95 | 30.70 | 30.90 | 44.51 | 35.75 | 36.60 | 45.56 | 39.20 | 37.71 | 30.00 | 39.47 | 37.02 |
| llama-2-7b-turbomind | 29.70 | 30.23 | 24.72 | 29.67 | 34.78 | 30.07 | 31.11 | 31.16 | 30.29 | 25.50 | 31.34 | 27.31 |
| llama-2-13b-turbomind | 30.83 | 32.56 | 24.16 | 42.31 | 45.41 | 32.87 | 36.67 | 45.23 | 38.29 | 33.50 | 35.17 | 34.31 |
| llama-2-70b-turbomind | 53.76 | 38.14 | 30.34 | 58.79 | 65.70 | 43.82 | 51.11 | 58.29 | 49.71 | 42.00 | 49.76 | 46.28 |
| llama-3-8b-turbomind | 52.63 | 42.33 | 27.53 | 51.65 | 65.70 | 44.52 | 54.44 | 51.26 | 46.86 | 43.00 | 46.41 | 45.15 |
| llama-3-70b-turbomind | 72.93 | 52.56 | 32.58 | 71.98 | 83.57 | 56.88 | 69.44 | 78.89 | 76.00 | 67.50 | 57.89 | 59.14 |
| internlm2-1.8b-turbomind | 51.50 | 38.14 | 25.84 | 56.04 | 71.50 | 47.32 | 35.00 | 43.72 | 42.29 | 39.00 | 41.15 | 36.57 |
| internlm2-7b-turbomind | 72.56 | 53.49 | 52.25 | 79.67 | 90.82 | 62.00 | 62.78 | 64.32 | 66.86 | 59.50 | 55.74 | 53.50 |
| internlm2-20b-turbomind | 74.06 | 54.42 | 56.18 | 81.87 | 92.27 | 61.77 | 68.33 | 69.85 | 68.00 | 63.50 | 60.77 | 58.92 |
| qwen-1.8b-turbomind | 54.14 | 43.72 | 39.89 | 69.23 | 85.02 | 49.88 | 45.56 | 48.74 | 48.57 | 51.50 | 46.89 | 45.82 |
| qwen-7b-turbomind | 71.05 | 48.37 | 53.93 | 81.87 | 93.72 | 59.67 | 54.44 | 62.31 | 58.29 | 57.50 | 50.24 | 56.66 |
| qwen-14b-turbomind | 79.70 | 53.02 | 63.48 | 87.36 | 94.20 | 71.33 | 63.33 | 71.36 | 73.14 | 68.00 | 59.09 | 67.95 |
| qwen-72b-turbomind | 90.23 | 77.21 | 79.21 | 91.76 | 96.14 | 77.86 | 86.11 | 85.43 | 91.43 | 90.50 | 76.08 | 86.68 |
| qwen1.5-0.5b-hf | 44.36 | 36.74 | 39.33 | 58.24 | 78.26 | 43.36 | 40.00 | 45.23 | 41.71 | 42.50 | 43.54 | 43.12 |
| qwen1.5-1.8b-hf | 59.40 | 47.91 | 37.08 | 72.53 | 91.30 | 53.61 | 53.33 | 51.26 | 49.71 | 58.00 | 51.20 | 56.21 |
| qwen1.5-4b-hf | 65.04 | 58.60 | 55.62 | 83.52 | 94.20 | 62.00 | 63.89 | 65.33 | 65.71 | 64.00 | 55.26 | 61.40 |
| qwen1.5-7b-hf | 78.57 | 66.51 | 66.85 | 87.91 | 94.69 | 68.07 | 65.00 | 64.82 | 77.14 | 77.50 | 60.77 | 74.49 |
| qwen1.5-14b-hf | 83.08 | 72.09 | 70.22 | 90.11 | 94.20 | 69.46 | 73.89 | 70.35 | 82.29 | 83.00 | 65.31 | 78.33 |
| qwen1.5-32b-hf | 87.59 | 78.14 | 79.78 | 92.86 | 95.65 | 78.32 | 80.56 | 79.90 | 90.29 | 89.00 | 77.27 | 86.68 |
| qwen1.5-72b-hf | 91.35 | 76.74 | 79.21 | 91.76 | 96.14 | 79.25 | 85.56 | 86.93 | 92.00 | 90.00 | 75.84 | 86.91 |
| qwen1.5-moe-a2-7b-hf | 88.35 | 75.81 | 51.12 | 79.12 | 94.69 | 67.37 | 80.56 | 73.37 | 87.43 | 84.00 | 78.23 | 82.39 |
| mistral-7b-v0.1-hf | 40.23 | 39.07 | 24.16 | 41.21 | 52.17 | 41.49 | 45.00 | 52.26 | 45.14 | 42.00 | 42.58 | 44.02 |
| mistral-7b-v0.2-hf | 36.84 | 34.88 | 23.03 | 43.96 | 52.66 | 40.79 | 50.00 | 50.75 | 45.14 | 40.50 | 42.58 | 40.86 |
| mixtral-8x7b-v0.1-hf | 47.74 | 40.00 | 28.09 | 57.14 | 58.94 | 44.29 | 58.33 | 53.77 | 48.57 | 46.00 | 51.20 | 46.50 |
| mixtral-8x22b-v0.1-hf | 59.02 | 41.86 | 29.78 | 60.99 | 71.01 | 50.82 | 57.78 | 67.34 | 62.29 | 52.00 | 53.35 | 55.98 |
| yi-6b-hf | 85.34 | 67.91 | 53.93 | 80.22 | 91.79 | 65.97 | 72.22 | 72.36 | 82.29 | 84.50 | 69.86 | 71.56 |
| yi-34b-hf | 94.36 | 76.74 | 65.73 | 87.91 | 95.17 | 79.25 | 85.56 | 90.95 | 90.86 | 92.00 | 76.79 | 82.39 |
| deepseek-7b-base-hf | 65.79 | 29.30 | 32.58 | 47.80 | 67.15 | 37.76 | 44.44 | 52.26 | 43.43 | 36.50 | 41.15 | 37.02 |
| deepseek-67b-base-hf | 83.83 | 58.60 | 45.51 | 79.67 | 90.34 | 62.47 | 70.56 | 70.85 | 81.14 | 71.50 | 61.72 | 60.05 |
| model | fire_engineer | environmental_impact_assessment_engineer | tax_accountant | physician |
|:------------------------:|----------------:|-------------------------------------------:|-----------------:|------------:|
| llama-7b-turbomind | 22.34 | 24.91 | 29.12 | 27.77 |
| llama-13b-turbomind | 24.11 | 30.25 | 27.77 | 30.70 |
| llama-30b-turbomind | 28.72 | 31.67 | 31.83 | 36.57 |
| llama-65b-turbomind | 28.37 | 39.15 | 33.63 | 35.44 |
| llama-2-7b-turbomind | 22.70 | 24.91 | 25.51 | 29.80 |
| llama-2-13b-turbomind | 25.53 | 35.94 | 29.35 | 35.44 |
| llama-2-70b-turbomind | 36.52 | 52.67 | 36.12 | 52.60 |
| llama-3-8b-turbomind | 35.46 | 49.82 | 41.31 | 55.30 |
| llama-3-70b-turbomind | 48.58 | 64.41 | 52.60 | 75.40 |
| internlm2-1.8b-turbomind | 32.27 | 42.35 | 39.05 | 45.15 |
| internlm2-7b-turbomind | 46.81 | 55.16 | 47.63 | 67.27 |
| internlm2-20b-turbomind | 45.04 | 62.63 | 51.47 | 69.75 |
| qwen-1.8b-turbomind | 41.84 | 47.69 | 45.60 | 57.34 |
| qwen-7b-turbomind | 41.84 | 54.80 | 48.08 | 69.53 |
| qwen-14b-turbomind | 45.74 | 64.77 | 56.43 | 77.88 |
| qwen-72b-turbomind | 80.50 | 74.73 | 81.04 | 89.62 |
| qwen1.5-0.5b-hf | 39.36 | 41.28 | 38.37 | 48.08 |
| qwen1.5-1.8b-hf | 45.74 | 49.47 | 51.69 | 63.43 |
| qwen1.5-4b-hf | 50.35 | 51.60 | 58.69 | 75.17 |
| qwen1.5-7b-hf | 58.51 | 65.84 | 67.04 | 81.94 |
| qwen1.5-14b-hf | 63.83 | 67.26 | 72.23 | 87.36 |
| qwen1.5-32b-hf | 74.47 | 73.31 | 80.14 | 90.74 |
| qwen1.5-72b-hf | 79.79 | 75.09 | 81.04 | 90.07 |
| qwen1.5-moe-a2-7b-hf | 74.82 | 77.58 | 79.68 | 91.65 |
| mistral-7b-v0.1-hf | 32.27 | 45.91 | 37.70 | 50.56 |
| mistral-7b-v0.2-hf | 32.62 | 44.13 | 36.79 | 46.28 |
| mixtral-8x7b-v0.1-hf | 35.11 | 53.02 | 46.73 | 52.37 |
| mixtral-8x22b-v0.1-hf | 38.65 | 56.23 | 49.21 | 59.82 |
| yi-6b-hf | 67.38 | 68.68 | 69.53 | 83.07 |
| yi-34b-hf | 77.66 | 83.27 | 77.43 | 89.84 |
| deepseek-7b-base-hf | 30.50 | 38.79 | 35.67 | 46.28 |
| deepseek-67b-base-hf | 46.81 | 65.12 | 54.40 | 77.65 |
### Details on Dev Split
## Chat Models
| model | ceval-test | ceval-test-hard | ceval-test-stem | ceval-test-social-science | ceval-test-humanities | ceval-test-other | ceval-dev | ceval-dev-hard | ceval-dev-stem | ceval-dev-social-science | ceval-dev-humanities | ceval-dev-other |
|:-----------------------------:|-------------:|------------------:|------------------:|----------------------------:|------------------------:|-------------------:|------------:|-----------------:|-----------------:|---------------------------:|-----------------------:|------------------:|
| qwen1.5-0.5b-chat-hf | 36.88 | 28.83 | 34.49 | 43.46 | 37.35 | 34.76 | 38.58 | 33.90 | 33.63 | 43.81 | 41.79 | 39.59 |
| qwen1.5-1.8b-chat-hf | 55.17 | 38.21 | 50.63 | 70.26 | 56.04 | 48.82 | 55.93 | 37.60 | 50.31 | 67.59 | 60.90 | 50.59 |
| qwen1.5-4b-chat-hf | 61.54 | 44.79 | 56.86 | 75.84 | 62.13 | 56.46 | 62.76 | 38.32 | 55.39 | 79.53 | 65.67 | 58.00 |
| qwen1.5-7b-chat-hf | 68.71 | 51.77 | 64.27 | 81.23 | 68.22 | 65.88 | 71.10 | 50.13 | 65.42 | 83.99 | 73.77 | 67.02 |
| qwen1.5-14b-chat-hf | 74.80 | 56.54 | 69.46 | 87.47 | 76.46 | 71.32 | 76.35 | 52.08 | 69.68 | 86.70 | 80.56 | 74.87 |
| qwen1.5-32b-chat-hf | 80.47 | 63.17 | 75.66 | 89.58 | 81.98 | 79.43 | 81.27 | 63.51 | 76.64 | 89.39 | 82.97 | 80.59 |
| qwen1.5-72b-chat-hf | 81.53 | 63.62 | 75.86 | 90.74 | 83.18 | 81.84 | 82.88 | 62.44 | 77.54 | 89.80 | 86.11 | 83.07 |
| qwen1.5-110b-chat-hf | 87.33 | 67.27 | 80.70 | 93.58 | 89.67 | 91.35 | 87.59 | 73.64 | 81.94 | 91.47 | 92.12 | 89.80 |
| internlm2-chat-1.8b-hf | 47.04 | 34.81 | 43.28 | 59.34 | 48.24 | 41.50 | 48.51 | 36.75 | 42.23 | 57.79 | 54.83 | 45.15 |
| internlm2-chat-1.8b-sft-hf | 47.19 | 35.34 | 43.49 | 59.56 | 48.30 | 41.58 | 48.75 | 35.83 | 42.04 | 59.80 | 54.84 | 44.83 |
| internlm2-chat-7b-hf | 58.75 | 39.61 | 52.38 | 71.46 | 61.57 | 55.96 | 61.04 | 36.56 | 51.81 | 74.01 | 69.13 | 57.92 |
| internlm2-chat-7b-sft-hf | 58.96 | 40.09 | 52.40 | 71.49 | 62.20 | 56.26 | 61.02 | 37.29 | 52.60 | 74.01 | 68.27 | 57.27 |
| internlm2-chat-20b-hf | 63.12 | 42.65 | 56.21 | 75.64 | 67.15 | 60.27 | 63.45 | 34.96 | 52.84 | 79.27 | 71.50 | 60.32 |
| internlm2-chat-20b-sft-hf | 63.16 | 42.70 | 56.19 | 75.74 | 67.20 | 60.37 | 63.54 | 34.96 | 52.57 | 80.33 | 71.42 | 60.34 |
| llama-3-8b-instruct-hf | 50.90 | 34.54 | 46.73 | 58.73 | 49.24 | 53.04 | 52.55 | 36.37 | 48.47 | 58.03 | 53.26 | 54.26 |
| llama-3-70b-instruct-hf | 67.38 | 54.02 | 65.16 | 76.83 | 62.29 | 67.92 | 67.92 | 54.50 | 66.85 | 76.80 | 65.98 | 63.72 |
| llama-3-8b-instruct-lmdeploy | 49.92 | 34.75 | 46.19 | 58.49 | 47.68 | 51.14 | 50.27 | 33.32 | 46.25 | 56.93 | 49.02 | 52.76 |
| llama-3-70b-instruct-lmdeploy | 66.41 | 52.76 | 64.72 | 75.31 | 61.36 | 66.44 | 68.21 | 52.28 | 65.86 | 75.06 | 68.37 | 66.09 |
| mistral-7b-instruct-v0.1-hf | 36.76 | 27.76 | 35.55 | 42.41 | 34.45 | 36.12 | 40.04 | 30.21 | 35.77 | 45.15 | 40.99 | 42.22 |
| mistral-7b-instruct-v0.2-hf | 40.38 | 30.26 | 38.82 | 47.66 | 37.08 | 39.91 | 43.00 | 25.97 | 38.60 | 47.44 | 48.15 | 41.82 |
| mixtral-8x7b-instruct-v0.1-hf | 49.61 | 37.78 | 47.86 | 58.56 | 46.40 | 47.85 | 51.68 | 37.41 | 49.14 | 59.79 | 52.97 | 47.65 |
### Details on Test Split
| model | computer_network | operating_system | computer_architecture | college_programming | college_physics | college_chemistry | advanced_mathematics | probability_and_statistics | discrete_mathematics | electrical_engineer | metrology_engineer | high_school_mathematics |
|:-----------------------------:|-------------------:|-------------------:|------------------------:|----------------------:|------------------:|--------------------:|-----------------------:|-----------------------------:|-----------------------:|----------------------:|---------------------:|--------------------------:|
| qwen1.5-0.5b-chat-hf | 35.67 | 36.87 | 33.68 | 33.92 | 35.23 | 28.12 | 27.17 | 26.51 | 24.84 | 28.91 | 40.18 | 25.90 |
| qwen1.5-1.8b-chat-hf | 46.78 | 47.49 | 50.78 | 39.18 | 41.48 | 31.25 | 32.95 | 27.71 | 28.10 | 34.81 | 55.71 | 27.11 |
| qwen1.5-4b-chat-hf | 54.39 | 54.75 | 54.92 | 44.74 | 46.02 | 43.30 | 39.31 | 31.33 | 28.10 | 45.13 | 58.90 | 43.98 |
| qwen1.5-7b-chat-hf | 60.82 | 60.34 | 63.21 | 55.85 | 48.86 | 45.09 | 46.24 | 36.14 | 39.22 | 47.49 | 70.32 | 45.78 |
| qwen1.5-14b-chat-hf | 69.59 | 62.57 | 64.77 | 64.91 | 55.68 | 57.14 | 49.13 | 32.53 | 43.14 | 55.16 | 76.71 | 46.99 |
| qwen1.5-32b-chat-hf | 81.87 | 74.30 | 73.58 | 71.35 | 63.07 | 60.71 | 50.87 | 46.99 | 47.06 | 59.29 | 83.11 | 60.84 |
| qwen1.5-72b-chat-hf | 77.78 | 75.42 | 76.17 | 73.39 | 63.64 | 62.50 | 45.09 | 45.78 | 48.37 | 59.00 | 81.74 | 60.84 |
| qwen1.5-110b-chat-hf | 83.63 | 86.03 | 81.87 | 77.49 | 76.70 | 67.86 | 49.13 | 47.59 | 55.56 | 79.94 | 95.89 | 62.05 |
| internlm2-chat-1.8b-hf | 42.11 | 43.58 | 44.56 | 35.38 | 32.95 | 34.82 | 32.95 | 28.92 | 32.68 | 34.22 | 53.42 | 31.93 |
| internlm2-chat-1.8b-sft-hf | 42.11 | 44.13 | 43.01 | 35.09 | 34.09 | 36.16 | 32.95 | 27.11 | 33.33 | 35.10 | 51.14 | 33.13 |
| internlm2-chat-7b-hf | 59.65 | 60.89 | 58.03 | 51.46 | 36.93 | 43.75 | 36.99 | 29.52 | 36.60 | 39.82 | 63.47 | 38.55 |
| internlm2-chat-7b-sft-hf | 59.06 | 61.45 | 56.48 | 52.63 | 39.77 | 41.52 | 36.99 | 27.71 | 39.22 | 40.12 | 62.10 | 40.36 |
| internlm2-chat-20b-hf | 61.99 | 70.39 | 63.73 | 54.97 | 33.52 | 47.77 | 43.93 | 40.96 | 44.44 | 44.25 | 61.64 | 34.34 |
| internlm2-chat-20b-sft-hf | 61.40 | 70.39 | 63.21 | 54.97 | 32.95 | 47.77 | 42.20 | 42.17 | 43.14 | 44.25 | 61.64 | 32.53 |
| llama-3-8b-instruct-hf | 57.31 | 58.10 | 57.51 | 51.17 | 28.41 | 35.27 | 39.31 | 32.53 | 35.29 | 38.05 | 55.25 | 27.11 |
| llama-3-70b-instruct-hf | 71.93 | 74.86 | 70.98 | 67.54 | 50.57 | 57.14 | 52.60 | 53.01 | 56.21 | 47.79 | 68.95 | 43.98 |
| llama-3-8b-instruct-lmdeploy | 55.56 | 57.54 | 55.44 | 48.25 | 30.11 | 33.04 | 35.84 | 31.33 | 33.33 | 38.94 | 53.88 | 31.93 |
| llama-3-70b-instruct-lmdeploy | 70.76 | 77.09 | 69.95 | 67.84 | 49.43 | 54.02 | 50.87 | 54.22 | 56.21 | 47.20 | 69.86 | 42.17 |
| mistral-7b-instruct-v0.1-hf | 49.12 | 47.49 | 43.52 | 39.18 | 32.39 | 28.57 | 29.48 | 24.10 | 28.10 | 37.46 | 44.29 | 23.49 |
| mistral-7b-instruct-v0.2-hf | 47.95 | 53.07 | 52.85 | 42.69 | 28.41 | 26.79 | 40.46 | 30.12 | 29.41 | 33.33 | 42.92 | 24.10 |
| mixtral-8x7b-instruct-v0.1-hf | 58.48 | 62.57 | 58.03 | 56.43 | 38.64 | 36.16 | 39.31 | 34.94 | 37.91 | 34.81 | 55.71 | 28.31 |
| model | high_school_physics | high_school_chemistry | high_school_biology | middle_school_mathematics | middle_school_biology | middle_school_physics | middle_school_chemistry | veterinary_medicine | college_economics | business_administration | marxism | mao_zedong_thought |
|:-----------------------------:|----------------------:|------------------------:|----------------------:|----------------------------:|------------------------:|------------------------:|--------------------------:|----------------------:|--------------------:|--------------------------:|----------:|---------------------:|
| qwen1.5-0.5b-chat-hf | 30.86 | 31.98 | 44.00 | 27.68 | 47.40 | 40.45 | 55.14 | 35.24 | 32.80 | 30.56 | 58.66 | 57.53 |
| qwen1.5-1.8b-chat-hf | 54.86 | 62.21 | 69.14 | 53.67 | 82.81 | 83.15 | 85.41 | 58.10 | 44.06 | 49.83 | 82.12 | 82.65 |
| qwen1.5-4b-chat-hf | 58.86 | 67.44 | 80.00 | 55.93 | 89.58 | 88.20 | 88.11 | 64.29 | 47.08 | 57.48 | 86.59 | 84.93 |
| qwen1.5-7b-chat-hf | 72.00 | 80.81 | 84.00 | 70.06 | 95.31 | 94.94 | 95.14 | 73.81 | 56.94 | 66.11 | 91.62 | 89.04 |
| qwen1.5-14b-chat-hf | 84.00 | 83.72 | 90.29 | 80.23 | 97.92 | 94.94 | 98.38 | 81.43 | 63.18 | 74.75 | 93.30 | 96.80 |
| qwen1.5-32b-chat-hf | 85.71 | 90.12 | 93.71 | 85.31 | 97.92 | 98.31 | 100.00 | 89.05 | 69.82 | 75.75 | 93.85 | 97.72 |
| qwen1.5-72b-chat-hf | 88.57 | 94.19 | 94.86 | 85.31 | 97.92 | 97.75 | 98.38 | 90.48 | 71.63 | 79.73 | 93.85 | 97.72 |
| qwen1.5-110b-chat-hf | 86.86 | 92.44 | 94.29 | 85.31 | 98.44 | 98.88 | 98.92 | 95.24 | 78.87 | 86.38 | 95.53 | 99.54 |
| internlm2-chat-1.8b-hf | 35.43 | 48.84 | 52.00 | 35.03 | 70.31 | 67.98 | 67.03 | 41.43 | 37.83 | 36.88 | 70.95 | 60.73 |
| internlm2-chat-1.8b-sft-hf | 37.71 | 48.26 | 53.14 | 34.46 | 71.35 | 67.98 | 67.57 | 41.90 | 38.63 | 37.54 | 72.63 | 60.27 |
| internlm2-chat-7b-hf | 46.29 | 48.26 | 60.57 | 46.89 | 78.65 | 71.91 | 71.35 | 68.10 | 50.30 | 50.83 | 77.09 | 76.26 |
| internlm2-chat-7b-sft-hf | 46.86 | 48.26 | 61.14 | 45.76 | 77.60 | 71.91 | 71.35 | 67.62 | 50.10 | 50.50 | 77.09 | 75.80 |
| internlm2-chat-20b-hf | 49.71 | 46.51 | 63.43 | 55.37 | 80.73 | 74.72 | 79.46 | 72.38 | 55.73 | 59.80 | 85.47 | 76.26 |
| internlm2-chat-20b-sft-hf | 53.71 | 47.09 | 64.00 | 55.37 | 80.73 | 73.60 | 78.92 | 73.81 | 55.53 | 60.13 | 85.47 | 75.80 |
| llama-3-8b-instruct-hf | 38.86 | 39.53 | 50.29 | 40.11 | 65.10 | 60.11 | 63.78 | 61.43 | 47.89 | 45.85 | 69.27 | 56.16 |
| llama-3-70b-instruct-hf | 63.43 | 55.23 | 69.71 | 68.36 | 85.42 | 80.90 | 78.38 | 86.19 | 69.01 | 65.12 | 83.24 | 82.65 |
| llama-3-8b-instruct-lmdeploy | 41.71 | 40.70 | 52.00 | 41.24 | 61.46 | 58.43 | 65.41 | 57.62 | 45.27 | 46.18 | 69.27 | 55.71 |
| llama-3-70b-instruct-lmdeploy | 61.71 | 53.49 | 70.86 | 64.97 | 88.02 | 83.71 | 77.30 | 84.76 | 68.21 | 60.80 | 80.45 | 79.91 |
| mistral-7b-instruct-v0.1-hf | 27.43 | 28.49 | 36.00 | 28.25 | 40.10 | 42.70 | 43.78 | 37.14 | 32.80 | 37.87 | 41.90 | 48.86 |
| mistral-7b-instruct-v0.2-hf | 33.14 | 29.65 | 44.00 | 31.07 | 47.92 | 44.94 | 49.19 | 44.29 | 37.02 | 40.86 | 53.63 | 48.40 |
| mixtral-8x7b-instruct-v0.1-hf | 46.29 | 40.70 | 54.86 | 42.37 | 58.85 | 60.67 | 57.84 | 54.29 | 50.10 | 46.51 | 69.27 | 52.51 |
| model | education_science | teacher_qualification | high_school_politics | high_school_geography | middle_school_politics | middle_school_geography | modern_chinese_history | ideological_and_moral_cultivation | logic | law | chinese_language_and_literature | art_studies |
|:-----------------------------:|--------------------:|------------------------:|-----------------------:|------------------------:|-------------------------:|--------------------------:|-------------------------:|------------------------------------:|--------:|------:|----------------------------------:|--------------:|
| qwen1.5-0.5b-chat-hf | 33.33 | 46.12 | 37.50 | 37.08 | 57.51 | 43.52 | 42.45 | 51.74 | 32.84 | 31.22 | 37.32 | 24.50 |
| qwen1.5-1.8b-chat-hf | 54.07 | 72.43 | 74.43 | 66.85 | 89.12 | 87.04 | 77.36 | 76.16 | 38.24 | 44.34 | 46.89 | 40.94 |
| qwen1.5-4b-chat-hf | 60.00 | 84.71 | 82.39 | 69.66 | 94.82 | 90.74 | 79.72 | 78.49 | 41.67 | 57.47 | 54.07 | 56.38 |
| qwen1.5-7b-chat-hf | 66.30 | 90.73 | 84.66 | 80.90 | 94.30 | 91.67 | 82.55 | 84.88 | 38.73 | 60.18 | 60.77 | 63.42 |
| qwen1.5-14b-chat-hf | 74.81 | 93.73 | 90.91 | 92.13 | 96.89 | 98.15 | 89.62 | 88.37 | 54.41 | 70.14 | 69.86 | 69.13 |
| qwen1.5-32b-chat-hf | 80.37 | 94.49 | 93.75 | 94.94 | 97.93 | 97.22 | 90.09 | 90.70 | 68.63 | 78.73 | 73.21 | 77.52 |
| qwen1.5-72b-chat-hf | 84.07 | 96.74 | 95.45 | 94.94 | 97.93 | 95.37 | 92.92 | 91.28 | 63.73 | 80.09 | 73.68 | 83.89 |
| qwen1.5-110b-chat-hf | 90.37 | 96.99 | 96.02 | 95.51 | 98.45 | 98.15 | 93.87 | 94.19 | 81.37 | 86.88 | 84.69 | 90.94 |
| internlm2-chat-1.8b-hf | 48.15 | 65.41 | 69.32 | 54.49 | 79.27 | 70.37 | 60.85 | 64.53 | 32.35 | 32.58 | 45.45 | 40.60 |
| internlm2-chat-1.8b-sft-hf | 48.15 | 64.91 | 69.89 | 53.93 | 79.27 | 70.37 | 61.32 | 63.95 | 33.82 | 29.86 | 45.45 | 39.93 |
| internlm2-chat-7b-hf | 66.67 | 85.21 | 73.30 | 66.85 | 91.19 | 76.85 | 70.28 | 75.58 | 42.16 | 50.68 | 60.77 | 70.47 |
| internlm2-chat-7b-sft-hf | 67.04 | 85.21 | 73.86 | 66.85 | 90.67 | 77.78 | 71.70 | 75.00 | 42.16 | 51.13 | 60.29 | 72.15 |
| internlm2-chat-20b-hf | 74.07 | 85.96 | 75.57 | 77.53 | 89.12 | 76.85 | 72.64 | 83.72 | 51.96 | 56.11 | 68.42 | 73.49 |
| internlm2-chat-20b-sft-hf | 73.70 | 85.46 | 76.70 | 78.09 | 89.64 | 76.85 | 72.17 | 84.88 | 50.00 | 56.56 | 66.99 | 75.17 |
| llama-3-8b-instruct-hf | 55.93 | 67.42 | 55.68 | 55.06 | 72.02 | 62.04 | 54.25 | 66.86 | 44.12 | 40.72 | 47.37 | 44.63 |
| llama-3-70b-instruct-hf | 71.11 | 84.21 | 74.43 | 73.03 | 84.97 | 80.56 | 69.81 | 78.49 | 57.35 | 50.68 | 57.89 | 64.43 |
| llama-3-8b-instruct-lmdeploy | 54.81 | 67.17 | 58.52 | 53.37 | 72.54 | 62.04 | 57.08 | 63.95 | 44.12 | 37.56 | 46.89 | 42.62 |
| llama-3-70b-instruct-lmdeploy | 70.37 | 82.96 | 72.16 | 71.91 | 83.94 | 82.41 | 69.34 | 77.91 | 55.39 | 50.68 | 56.46 | 64.09 |
| mistral-7b-instruct-v0.1-hf | 39.63 | 46.62 | 33.52 | 41.01 | 56.48 | 45.37 | 36.32 | 43.60 | 29.90 | 31.67 | 39.71 | 31.88 |
| mistral-7b-instruct-v0.2-hf | 46.30 | 54.39 | 39.20 | 43.26 | 61.66 | 51.85 | 35.38 | 55.23 | 28.92 | 35.29 | 37.80 | 29.19 |
| mixtral-8x7b-instruct-v0.1-hf | 58.52 | 66.17 | 56.82 | 57.30 | 66.32 | 62.04 | 48.11 | 66.28 | 41.67 | 37.10 | 46.41 | 35.91 |
| model | professional_tour_guide | legal_professional | high_school_chinese | high_school_history | middle_school_history | civil_servant | sports_science | plant_protection | basic_medicine | clinical_medicine | urban_and_rural_planner | accountant |
|:-----------------------------:|--------------------------:|---------------------:|----------------------:|----------------------:|------------------------:|----------------:|-----------------:|-------------------:|-----------------:|--------------------:|--------------------------:|-------------:|
| qwen1.5-0.5b-chat-hf | 36.47 | 39.07 | 27.53 | 41.76 | 45.89 | 39.63 | 35.56 | 31.66 | 37.71 | 34.00 | 32.78 | 37.25 |
| qwen1.5-1.8b-chat-hf | 56.02 | 45.58 | 39.33 | 67.03 | 84.54 | 49.42 | 48.89 | 51.76 | 47.43 | 50.50 | 45.69 | 52.14 |
| qwen1.5-4b-chat-hf | 61.28 | 52.56 | 42.70 | 73.08 | 85.99 | 55.48 | 59.44 | 55.28 | 60.57 | 57.00 | 50.00 | 58.01 |
| qwen1.5-7b-chat-hf | 73.31 | 56.28 | 58.99 | 82.97 | 88.41 | 64.57 | 66.67 | 63.82 | 77.14 | 75.50 | 57.42 | 69.07 |
| qwen1.5-14b-chat-hf | 80.83 | 65.12 | 70.79 | 89.56 | 93.24 | 67.60 | 72.78 | 68.34 | 80.57 | 80.00 | 61.72 | 75.62 |
| qwen1.5-32b-chat-hf | 87.59 | 72.56 | 76.40 | 90.66 | 95.65 | 74.36 | 80.00 | 80.40 | 86.86 | 84.00 | 74.88 | 85.33 |
| qwen1.5-72b-chat-hf | 90.98 | 76.28 | 75.84 | 90.66 | 95.65 | 75.52 | 84.44 | 82.91 | 91.43 | 89.00 | 73.92 | 85.10 |
| qwen1.5-110b-chat-hf | 95.11 | 88.37 | 82.58 | 91.76 | 96.62 | 87.65 | 91.67 | 90.95 | 93.71 | 95.00 | 87.08 | 91.87 |
| internlm2-chat-1.8b-hf | 54.14 | 40.00 | 27.53 | 62.09 | 70.53 | 44.99 | 41.67 | 51.76 | 45.71 | 39.00 | 40.67 | 39.28 |
| internlm2-chat-1.8b-sft-hf | 54.14 | 42.33 | 26.97 | 61.54 | 71.98 | 45.45 | 41.67 | 50.25 | 45.14 | 37.50 | 41.39 | 40.63 |
| internlm2-chat-7b-hf | 70.68 | 44.19 | 34.83 | 73.63 | 84.06 | 51.98 | 57.22 | 68.34 | 66.86 | 57.50 | 54.55 | 50.11 |
| internlm2-chat-7b-sft-hf | 71.80 | 44.65 | 37.64 | 73.63 | 84.06 | 51.98 | 57.78 | 67.84 | 65.71 | 60.50 | 54.55 | 50.11 |
| internlm2-chat-20b-hf | 75.56 | 54.42 | 42.13 | 74.73 | 85.51 | 57.34 | 65.56 | 67.84 | 73.71 | 64.00 | 57.89 | 55.98 |
| internlm2-chat-20b-sft-hf | 76.32 | 55.35 | 41.01 | 75.27 | 85.51 | 58.28 | 65.56 | 67.34 | 72.57 | 65.00 | 58.37 | 56.43 |
| llama-3-8b-instruct-hf | 53.01 | 44.65 | 33.15 | 46.70 | 66.18 | 45.22 | 58.89 | 61.81 | 62.86 | 57.50 | 48.33 | 49.89 |
| llama-3-70b-instruct-hf | 71.43 | 50.70 | 30.90 | 71.43 | 82.13 | 59.67 | 73.33 | 73.37 | 82.86 | 82.00 | 59.09 | 62.08 |
| llama-3-8b-instruct-lmdeploy | 51.13 | 45.12 | 29.78 | 43.96 | 62.32 | 47.09 | 56.11 | 54.77 | 56.00 | 56.00 | 49.04 | 47.40 |
| llama-3-70b-instruct-lmdeploy | 68.80 | 48.84 | 30.90 | 70.88 | 81.64 | 58.28 | 72.22 | 70.85 | 80.00 | 81.00 | 57.66 | 62.53 |
| mistral-7b-instruct-v0.1-hf | 30.45 | 35.81 | 24.72 | 40.11 | 34.78 | 30.77 | 43.89 | 38.69 | 36.57 | 32.50 | 44.74 | 34.09 |
| mistral-7b-instruct-v0.2-hf | 36.09 | 38.14 | 23.03 | 43.41 | 45.41 | 35.90 | 50.00 | 41.71 | 42.86 | 36.00 | 45.22 | 42.21 |
| mixtral-8x7b-instruct-v0.1-hf | 47.37 | 44.65 | 30.34 | 51.65 | 60.87 | 42.19 | 53.89 | 58.29 | 52.00 | 47.00 | 48.56 | 44.02 |
| model | fire_engineer | environmental_impact_assessment_engineer | tax_accountant | physician |
|:-----------------------------:|----------------:|-------------------------------------------:|-----------------:|------------:|
| qwen1.5-0.5b-chat-hf | 27.66 | 38.43 | 32.28 | 35.44 |
| qwen1.5-1.8b-chat-hf | 38.65 | 46.62 | 46.73 | 59.14 |
| qwen1.5-4b-chat-hf | 49.29 | 54.80 | 51.02 | 70.20 |
| qwen1.5-7b-chat-hf | 53.90 | 62.28 | 57.79 | 76.52 |
| qwen1.5-14b-chat-hf | 58.87 | 65.12 | 67.27 | 86.68 |
| qwen1.5-32b-chat-hf | 74.11 | 70.82 | 74.94 | 88.04 |
| qwen1.5-72b-chat-hf | 74.82 | 75.09 | 78.56 | 89.39 |
| qwen1.5-110b-chat-hf | 88.30 | 88.97 | 94.13 | 95.49 |
| internlm2-chat-1.8b-hf | 30.14 | 41.99 | 34.54 | 46.73 |
| internlm2-chat-1.8b-sft-hf | 30.14 | 43.06 | 34.31 | 47.86 |
| internlm2-chat-7b-hf | 42.20 | 52.31 | 47.63 | 66.82 |
| internlm2-chat-7b-sft-hf | 43.26 | 52.67 | 47.86 | 66.59 |
| internlm2-chat-20b-hf | 45.74 | 54.80 | 51.02 | 69.07 |
| internlm2-chat-20b-sft-hf | 45.74 | 55.16 | 51.02 | 68.62 |
| llama-3-8b-instruct-hf | 37.59 | 50.53 | 42.44 | 68.40 |
| llama-3-70b-instruct-hf | 50.71 | 64.06 | 55.53 | 84.42 |
| llama-3-8b-instruct-lmdeploy | 37.94 | 50.53 | 41.53 | 66.14 |
| llama-3-70b-instruct-lmdeploy | 48.94 | 63.70 | 53.95 | 81.72 |
| mistral-7b-instruct-v0.1-hf | 27.66 | 39.15 | 29.35 | 39.95 |
| mistral-7b-instruct-v0.2-hf | 32.27 | 37.01 | 32.96 | 42.89 |
| mixtral-8x7b-instruct-v0.1-hf | 36.88 | 48.75 | 41.76 | 53.05 |
### Details on Dev Split
opencompass/configs/datasets/ceval/ceval_clean_ppl.py 0 → 100644
View file @ be3dfa50
from typing import List
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import FixKRetriever
from opencompass.openicl.icl_inferencer import PPLInferencer
from opencompass.openicl.icl_evaluator import AccContaminationEvaluator
from opencompass.datasets import CEvalDatasetClean as CEvalDataset
ceval_subject_mapping = {
'computer_network': ['Computer Network', '计算机网络', 'STEM'],
'operating_system': ['Operating System', '操作系统', 'STEM'],
'computer_architecture': ['Computer Architecture', '计算机组成', 'STEM'],
'college_programming': ['College Programming', '大学编程', 'STEM'],
'college_physics': ['College Physics', '大学物理', 'STEM'],
'college_chemistry': ['College Chemistry', '大学化学', 'STEM'],
'advanced_mathematics': ['Advanced Mathematics', '高等数学', 'STEM'],
'probability_and_statistics': ['Probability and Statistics', '概率统计', 'STEM'],
'discrete_mathematics': ['Discrete Mathematics', '离散数学', 'STEM'],
'electrical_engineer': ['Electrical Engineer', '注册电气工程师', 'STEM'],
'metrology_engineer': ['Metrology Engineer', '注册计量师', 'STEM'],
'high_school_mathematics': ['High School Mathematics', '高中数学', 'STEM'],
'high_school_physics': ['High School Physics', '高中物理', 'STEM'],
'high_school_chemistry': ['High School Chemistry', '高中化学', 'STEM'],
'high_school_biology': ['High School Biology', '高中生物', 'STEM'],
'middle_school_mathematics': ['Middle School Mathematics', '初中数学', 'STEM'],
'middle_school_biology': ['Middle School Biology', '初中生物', 'STEM'],
'middle_school_physics': ['Middle School Physics', '初中物理', 'STEM'],
'middle_school_chemistry': ['Middle School Chemistry', '初中化学', 'STEM'],
'veterinary_medicine': ['Veterinary Medicine', '兽医学', 'STEM'],
'college_economics': ['College Economics', '大学经济学', 'Social Science'],
'business_administration': ['Business Administration', '工商管理', 'Social Science'],
'marxism': ['Marxism', '马克思主义基本原理', 'Social Science'],
'mao_zedong_thought': ['Mao Zedong Thought', '毛泽东思想和中国特色社会主义理论体系概论', 'Social Science'],
'education_science': ['Education Science', '教育学', 'Social Science'],
'teacher_qualification': ['Teacher Qualification', '教师资格', 'Social Science'],
'high_school_politics': ['High School Politics', '高中政治', 'Social Science'],
'high_school_geography': ['High School Geography', '高中地理', 'Social Science'],
'middle_school_politics': ['Middle School Politics', '初中政治', 'Social Science'],
'middle_school_geography': ['Middle School Geography', '初中地理', 'Social Science'],
'modern_chinese_history': ['Modern Chinese History', '近代史纲要', 'Humanities'],
'ideological_and_moral_cultivation': ['Ideological and Moral Cultivation', '思想道德修养与法律基础', 'Humanities'],
'logic': ['Logic', '逻辑学', 'Humanities'],
'law': ['Law', '法学', 'Humanities'],
'chinese_language_and_literature': ['Chinese Language and Literature', '中国语言文学', 'Humanities'],
'art_studies': ['Art Studies', '艺术学', 'Humanities'],
'professional_tour_guide': ['Professional Tour Guide', '导游资格', 'Humanities'],
'legal_professional': ['Legal Professional', '法律职业资格', 'Humanities'],
'high_school_chinese': ['High School Chinese', '高中语文', 'Humanities'],
'high_school_history': ['High School History', '高中历史', 'Humanities'],
'middle_school_history': ['Middle School History', '初中历史', 'Humanities'],
'civil_servant': ['Civil Servant', '公务员', 'Other'],
'sports_science': ['Sports Science', '体育学', 'Other'],
'plant_protection': ['Plant Protection', '植物保护', 'Other'],
'basic_medicine': ['Basic Medicine', '基础医学', 'Other'],
'clinical_medicine': ['Clinical Medicine', '临床医学', 'Other'],
'urban_and_rural_planner': ['Urban and Rural Planner', '注册城乡规划师', 'Other'],
'accountant': ['Accountant', '注册会计师', 'Other'],
'fire_engineer': ['Fire Engineer', '注册消防工程师', 'Other'],
'environmental_impact_assessment_engineer': ['Environmental Impact Assessment Engineer', '环境影响评价工程师', 'Other'],
'tax_accountant': ['Tax Accountant', '税务师', 'Other'],
'physician': ['Physician', '医师资格', 'Other'],
}
ceval_all_sets = list(ceval_subject_mapping.keys())
ceval_datasets = []
for _split in ['val']:
for _name in ceval_all_sets:
_ch_name = ceval_subject_mapping[_name][1]
ceval_infer_cfg = dict(
ice_template=dict(
type=PromptTemplate,
template={
answer: dict(
begin='</E>',
round=[
dict(
role='HUMAN',
prompt=
f'以下是中国关于{_ch_name}考试的单项选择题，请选出其中的正确答案。\n{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案: '
),
dict(role='BOT', prompt=answer),
])
for answer in ['A', 'B', 'C', 'D']
},
ice_token='</E>',
),
retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
inferencer=dict(type=PPLInferencer),
)
ceval_eval_cfg = dict(evaluator=dict(type=AccContaminationEvaluator), analyze_contamination=True)
ceval_datasets.append(
dict(
type=CEvalDataset,
path='opencompass/ceval-exam',
name=_name,
abbr='ceval-' + _name if _split == 'val' else 'ceval-test-' + _name,
reader_cfg=dict(
input_columns=['question', 'A', 'B', 'C', 'D'],
output_column='answer',
train_split='dev',
test_split=_split),
infer_cfg=ceval_infer_cfg,
eval_cfg=ceval_eval_cfg,
))
del _split, _name, _ch_name
opencompass/configs/datasets/ceval/ceval_gen.py 0 → 100644
View file @ be3dfa50
from mmengine.config import read_base
with read_base():
from .ceval_gen_5f30c7 import ceval_datasets # noqa: F401, F403
opencompass/configs/datasets/ceval/ceval_gen_2daf24.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import FixKRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import CEvalDataset
from opencompass.utils.text_postprocessors import first_capital_postprocess
ceval_subject_mapping = {
'computer_network': ['Computer Network', '计算机网络', 'STEM'],
'operating_system': ['Operating System', '操作系统', 'STEM'],
'computer_architecture': ['Computer Architecture', '计算机组成', 'STEM'],
'college_programming': ['College Programming', '大学编程', 'STEM'],
'college_physics': ['College Physics', '大学物理', 'STEM'],
'college_chemistry': ['College Chemistry', '大学化学', 'STEM'],
'advanced_mathematics': ['Advanced Mathematics', '高等数学', 'STEM'],
'probability_and_statistics': ['Probability and Statistics', '概率统计', 'STEM'],
'discrete_mathematics': ['Discrete Mathematics', '离散数学', 'STEM'],
'electrical_engineer': ['Electrical Engineer', '注册电气工程师', 'STEM'],
'metrology_engineer': ['Metrology Engineer', '注册计量师', 'STEM'],
'high_school_mathematics': ['High School Mathematics', '高中数学', 'STEM'],
'high_school_physics': ['High School Physics', '高中物理', 'STEM'],
'high_school_chemistry': ['High School Chemistry', '高中化学', 'STEM'],
'high_school_biology': ['High School Biology', '高中生物', 'STEM'],
'middle_school_mathematics': ['Middle School Mathematics', '初中数学', 'STEM'],
'middle_school_biology': ['Middle School Biology', '初中生物', 'STEM'],
'middle_school_physics': ['Middle School Physics', '初中物理', 'STEM'],
'middle_school_chemistry': ['Middle School Chemistry', '初中化学', 'STEM'],
'veterinary_medicine': ['Veterinary Medicine', '兽医学', 'STEM'],
'college_economics': ['College Economics', '大学经济学', 'Social Science'],
'business_administration': ['Business Administration', '工商管理', 'Social Science'],
'marxism': ['Marxism', '马克思主义基本原理', 'Social Science'],
'mao_zedong_thought': ['Mao Zedong Thought', '毛泽东思想和中国特色社会主义理论体系概论', 'Social Science'],
'education_science': ['Education Science', '教育学', 'Social Science'],
'teacher_qualification': ['Teacher Qualification', '教师资格', 'Social Science'],
'high_school_politics': ['High School Politics', '高中政治', 'Social Science'],
'high_school_geography': ['High School Geography', '高中地理', 'Social Science'],
'middle_school_politics': ['Middle School Politics', '初中政治', 'Social Science'],
'middle_school_geography': ['Middle School Geography', '初中地理', 'Social Science'],
'modern_chinese_history': ['Modern Chinese History', '近代史纲要', 'Humanities'],
'ideological_and_moral_cultivation': ['Ideological and Moral Cultivation', '思想道德修养与法律基础', 'Humanities'],
'logic': ['Logic', '逻辑学', 'Humanities'],
'law': ['Law', '法学', 'Humanities'],
'chinese_language_and_literature': ['Chinese Language and Literature', '中国语言文学', 'Humanities'],
'art_studies': ['Art Studies', '艺术学', 'Humanities'],
'professional_tour_guide': ['Professional Tour Guide', '导游资格', 'Humanities'],
'legal_professional': ['Legal Professional', '法律职业资格', 'Humanities'],
'high_school_chinese': ['High School Chinese', '高中语文', 'Humanities'],
'high_school_history': ['High School History', '高中历史', 'Humanities'],
'middle_school_history': ['Middle School History', '初中历史', 'Humanities'],
'civil_servant': ['Civil Servant', '公务员', 'Other'],
'sports_science': ['Sports Science', '体育学', 'Other'],
'plant_protection': ['Plant Protection', '植物保护', 'Other'],
'basic_medicine': ['Basic Medicine', '基础医学', 'Other'],
'clinical_medicine': ['Clinical Medicine', '临床医学', 'Other'],
'urban_and_rural_planner': ['Urban and Rural Planner', '注册城乡规划师', 'Other'],
'accountant': ['Accountant', '注册会计师', 'Other'],
'fire_engineer': ['Fire Engineer', '注册消防工程师', 'Other'],
'environmental_impact_assessment_engineer': ['Environmental Impact Assessment Engineer', '环境影响评价工程师', 'Other'],
'tax_accountant': ['Tax Accountant', '税务师', 'Other'],
'physician': ['Physician', '医师资格', 'Other'],
}
ceval_all_sets = list(ceval_subject_mapping.keys())
ceval_datasets = []
for _split in ['val', 'test']:
for _name in ceval_all_sets:
_ch_name = ceval_subject_mapping[_name][1]
ceval_infer_cfg = dict(
ice_template=dict(
type=PromptTemplate,
template=dict(
begin='</E>',
round=[
dict(
role='HUMAN',
prompt=
f'以下是中国关于{_ch_name}考试的单项选择题，请选出其中的正确答案。\n{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案: '
),
dict(role='BOT', prompt='{answer}'),
]),
ice_token='</E>',
),
retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
inferencer=dict(type=GenInferencer),
)
ceval_eval_cfg = dict(
evaluator=dict(type=AccEvaluator),
pred_postprocessor=dict(type=first_capital_postprocess))
ceval_datasets.append(
dict(
type=CEvalDataset,
path='opencompass/ceval-exam',
name=_name,
abbr='ceval-' + _name if _split == 'val' else 'ceval-test-' +
_name,
reader_cfg=dict(
input_columns=['question', 'A', 'B', 'C', 'D'],
output_column='answer',
train_split='dev',
test_split=_split),
infer_cfg=ceval_infer_cfg,
eval_cfg=ceval_eval_cfg,
))
del _split, _name, _ch_name
opencompass/configs/datasets/ceval/ceval_gen_5f30c7.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import FixKRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import CEvalDataset
from opencompass.utils.text_postprocessors import first_capital_postprocess
ceval_subject_mapping = {
'computer_network': ['Computer Network', '计算机网络', 'STEM'],
'operating_system': ['Operating System', '操作系统', 'STEM'],
'computer_architecture': ['Computer Architecture', '计算机组成', 'STEM'],
'college_programming': ['College Programming', '大学编程', 'STEM'],
'college_physics': ['College Physics', '大学物理', 'STEM'],
'college_chemistry': ['College Chemistry', '大学化学', 'STEM'],
'advanced_mathematics': ['Advanced Mathematics', '高等数学', 'STEM'],
'probability_and_statistics': ['Probability and Statistics', '概率统计', 'STEM'],
'discrete_mathematics': ['Discrete Mathematics', '离散数学', 'STEM'],
'electrical_engineer': ['Electrical Engineer', '注册电气工程师', 'STEM'],
'metrology_engineer': ['Metrology Engineer', '注册计量师', 'STEM'],
'high_school_mathematics': ['High School Mathematics', '高中数学', 'STEM'],
'high_school_physics': ['High School Physics', '高中物理', 'STEM'],
'high_school_chemistry': ['High School Chemistry', '高中化学', 'STEM'],
'high_school_biology': ['High School Biology', '高中生物', 'STEM'],
'middle_school_mathematics': ['Middle School Mathematics', '初中数学', 'STEM'],
'middle_school_biology': ['Middle School Biology', '初中生物', 'STEM'],
'middle_school_physics': ['Middle School Physics', '初中物理', 'STEM'],
'middle_school_chemistry': ['Middle School Chemistry', '初中化学', 'STEM'],
'veterinary_medicine': ['Veterinary Medicine', '兽医学', 'STEM'],
'college_economics': ['College Economics', '大学经济学', 'Social Science'],
'business_administration': ['Business Administration', '工商管理', 'Social Science'],
'marxism': ['Marxism', '马克思主义基本原理', 'Social Science'],
'mao_zedong_thought': ['Mao Zedong Thought', '毛泽东思想和中国特色社会主义理论体系概论', 'Social Science'],
'education_science': ['Education Science', '教育学', 'Social Science'],
'teacher_qualification': ['Teacher Qualification', '教师资格', 'Social Science'],
'high_school_politics': ['High School Politics', '高中政治', 'Social Science'],
'high_school_geography': ['High School Geography', '高中地理', 'Social Science'],
'middle_school_politics': ['Middle School Politics', '初中政治', 'Social Science'],
'middle_school_geography': ['Middle School Geography', '初中地理', 'Social Science'],
'modern_chinese_history': ['Modern Chinese History', '近代史纲要', 'Humanities'],
'ideological_and_moral_cultivation': ['Ideological and Moral Cultivation', '思想道德修养与法律基础', 'Humanities'],
'logic': ['Logic', '逻辑学', 'Humanities'],
'law': ['Law', '法学', 'Humanities'],
'chinese_language_and_literature': ['Chinese Language and Literature', '中国语言文学', 'Humanities'],
'art_studies': ['Art Studies', '艺术学', 'Humanities'],
'professional_tour_guide': ['Professional Tour Guide', '导游资格', 'Humanities'],
'legal_professional': ['Legal Professional', '法律职业资格', 'Humanities'],
'high_school_chinese': ['High School Chinese', '高中语文', 'Humanities'],
'high_school_history': ['High School History', '高中历史', 'Humanities'],
'middle_school_history': ['Middle School History', '初中历史', 'Humanities'],
'civil_servant': ['Civil Servant', '公务员', 'Other'],
'sports_science': ['Sports Science', '体育学', 'Other'],
'plant_protection': ['Plant Protection', '植物保护', 'Other'],
'basic_medicine': ['Basic Medicine', '基础医学', 'Other'],
'clinical_medicine': ['Clinical Medicine', '临床医学', 'Other'],
'urban_and_rural_planner': ['Urban and Rural Planner', '注册城乡规划师', 'Other'],
'accountant': ['Accountant', '注册会计师', 'Other'],
'fire_engineer': ['Fire Engineer', '注册消防工程师', 'Other'],
'environmental_impact_assessment_engineer': ['Environmental Impact Assessment Engineer', '环境影响评价工程师', 'Other'],
'tax_accountant': ['Tax Accountant', '税务师', 'Other'],
'physician': ['Physician', '医师资格', 'Other'],
}
ceval_all_sets = list(ceval_subject_mapping.keys())
ceval_datasets = []
for _split in ['val']:
for _name in ceval_all_sets:
_ch_name = ceval_subject_mapping[_name][1]
ceval_infer_cfg = dict(
ice_template=dict(
type=PromptTemplate,
template=dict(
begin='</E>',
round=[
dict(
role='HUMAN',
prompt=
f'以下是中国关于{_ch_name}考试的单项选择题，请选出其中的正确答案。\n{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案: '
),
dict(role='BOT', prompt='{answer}'),
]),
ice_token='</E>',
),
retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
inferencer=dict(type=GenInferencer),
)
ceval_eval_cfg = dict(
evaluator=dict(type=AccEvaluator),
pred_postprocessor=dict(type=first_capital_postprocess))
ceval_datasets.append(
dict(
type=CEvalDataset,
path='opencompass/ceval-exam',
name=_name,
abbr='ceval-' + _name if _split == 'val' else 'ceval-test-' +
_name,
reader_cfg=dict(
input_columns=['question', 'A', 'B', 'C', 'D'],
output_column='answer',
train_split='dev',
test_split=_split),
infer_cfg=ceval_infer_cfg,
eval_cfg=ceval_eval_cfg,
))
del _split, _name, _ch_name
opencompass/configs/datasets/ceval/ceval_internal_ppl_1cd8bf.py 0 → 100644
View file @ be3dfa50
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import FixKRetriever
from opencompass.openicl.icl_inferencer import PPLInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import CEvalDataset
ceval_subject_mapping = {
'computer_network': ['Computer Network', '计算机网络', 'STEM'],
'operating_system': ['Operating System', '操作系统', 'STEM'],
'computer_architecture': ['Computer Architecture', '计算机组成', 'STEM'],
'college_programming': ['College Programming', '大学编程', 'STEM'],
'college_physics': ['College Physics', '大学物理', 'STEM'],
'college_chemistry': ['College Chemistry', '大学化学', 'STEM'],
'advanced_mathematics': ['Advanced Mathematics', '高等数学', 'STEM'],
'probability_and_statistics': ['Probability and Statistics', '概率统计', 'STEM'],
'discrete_mathematics': ['Discrete Mathematics', '离散数学', 'STEM'],
'electrical_engineer': ['Electrical Engineer', '注册电气工程师', 'STEM'],
'metrology_engineer': ['Metrology Engineer', '注册计量师', 'STEM'],
'high_school_mathematics': ['High School Mathematics', '高中数学', 'STEM'],
'high_school_physics': ['High School Physics', '高中物理', 'STEM'],
'high_school_chemistry': ['High School Chemistry', '高中化学', 'STEM'],
'high_school_biology': ['High School Biology', '高中生物', 'STEM'],
'middle_school_mathematics': ['Middle School Mathematics', '初中数学', 'STEM'],
'middle_school_biology': ['Middle School Biology', '初中生物', 'STEM'],
'middle_school_physics': ['Middle School Physics', '初中物理', 'STEM'],
'middle_school_chemistry': ['Middle School Chemistry', '初中化学', 'STEM'],
'veterinary_medicine': ['Veterinary Medicine', '兽医学', 'STEM'],
'college_economics': ['College Economics', '大学经济学', 'Social Science'],
'business_administration': ['Business Administration', '工商管理', 'Social Science'],
'marxism': ['Marxism', '马克思主义基本原理', 'Social Science'],
'mao_zedong_thought': ['Mao Zedong Thought', '毛泽东思想和中国特色社会主义理论体系概论', 'Social Science'],
'education_science': ['Education Science', '教育学', 'Social Science'],
'teacher_qualification': ['Teacher Qualification', '教师资格', 'Social Science'],
'high_school_politics': ['High School Politics', '高中政治', 'Social Science'],
'high_school_geography': ['High School Geography', '高中地理', 'Social Science'],
'middle_school_politics': ['Middle School Politics', '初中政治', 'Social Science'],
'middle_school_geography': ['Middle School Geography', '初中地理', 'Social Science'],
'modern_chinese_history': ['Modern Chinese History', '近代史纲要', 'Humanities'],
'ideological_and_moral_cultivation': ['Ideological and Moral Cultivation', '思想道德修养与法律基础', 'Humanities'],
'logic': ['Logic', '逻辑学', 'Humanities'],
'law': ['Law', '法学', 'Humanities'],
'chinese_language_and_literature': ['Chinese Language and Literature', '中国语言文学', 'Humanities'],
'art_studies': ['Art Studies', '艺术学', 'Humanities'],
'professional_tour_guide': ['Professional Tour Guide', '导游资格', 'Humanities'],
'legal_professional': ['Legal Professional', '法律职业资格', 'Humanities'],
'high_school_chinese': ['High School Chinese', '高中语文', 'Humanities'],
'high_school_history': ['High School History', '高中历史', 'Humanities'],
'middle_school_history': ['Middle School History', '初中历史', 'Humanities'],
'civil_servant': ['Civil Servant', '公务员', 'Other'],
'sports_science': ['Sports Science', '体育学', 'Other'],
'plant_protection': ['Plant Protection', '植物保护', 'Other'],
'basic_medicine': ['Basic Medicine', '基础医学', 'Other'],
'clinical_medicine': ['Clinical Medicine', '临床医学', 'Other'],
'urban_and_rural_planner': ['Urban and Rural Planner', '注册城乡规划师', 'Other'],
'accountant': ['Accountant', '注册会计师', 'Other'],
'fire_engineer': ['Fire Engineer', '注册消防工程师', 'Other'],
'environmental_impact_assessment_engineer': ['Environmental Impact Assessment Engineer', '环境影响评价工程师', 'Other'],
'tax_accountant': ['Tax Accountant', '税务师', 'Other'],
'physician': ['Physician', '医师资格', 'Other'],
}
ceval_all_sets = list(ceval_subject_mapping.keys())
ceval_datasets = []
for _split in ['val', 'test']:
for _name in ceval_all_sets:
ceval_reader_cfg = dict(
input_columns=['question', 'A', 'B', 'C', 'D'],
output_column='answer',
train_split='dev',
test_split=_split,
)
_ch_name = ceval_subject_mapping[_name][1]
hint = f'以下是关于{_ch_name}的单项选择题，请直接给出正确答案的选项。'
question_and_options = '{question}\nA. {A}\nB. {B}\nC. {C}\nD. {D}'
ceval_infer_cfg = dict(
ice_template=dict(
type=PromptTemplate,
template={answer: f'{question_and_options}\n答案: {answer}\n' for answer in ['A', 'B', 'C', 'D']},
),
prompt_template=dict(
type=PromptTemplate,
template={answer: f'{hint}\n</E>{question_and_options}\n答案: {answer}' for answer in ['A', 'B', 'C', 'D']},
ice_token='</E>',
),
retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
inferencer=dict(type=PPLInferencer),
)
ceval_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
ceval_datasets.append(
dict(
type=CEvalDataset,
path='opencompass/ceval-exam',
name=_name,
abbr='ceval-' + _name if _split == 'val' else 'ceval-test-' + _name,
reader_cfg=ceval_reader_cfg,
infer_cfg=ceval_infer_cfg,
eval_cfg=ceval_eval_cfg,
))
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment