From 8a5029b1219ebdd3f3c5e4d30a25bc4e3851f058 Mon Sep 17 00:00:00 2001 From: Dongsheng Zhu <59612926+Zhudongsheng75@users.noreply.github.com> Date: Fri, 21 Mar 2025 20:09:25 +0800 Subject: [PATCH 1/6] [Feature] Add MultiPL-E & Code Evaluator (#1963) * multiple_code develop * multiple_code update * comments upadate * index upadate --- dataset-index.yml | 5 + .../multipl_e/multiple_top_ten_gen.py | 56 ++++ opencompass/configs/models/phi/hf_phi_4.py | 12 + opencompass/datasets/__init__.py | 1 + opencompass/datasets/custom.py | 27 ++ opencompass/datasets/multipl_e.py | 103 +++++++ .../openicl/icl_evaluator/code_evaluator.py | 267 ++++++++++++++++++ opencompass/utils/datasets_info.py | 11 + 8 files changed, 482 insertions(+) create mode 100644 opencompass/configs/datasets/multipl_e/multiple_top_ten_gen.py create mode 100644 opencompass/configs/models/phi/hf_phi_4.py create mode 100644 opencompass/datasets/multipl_e.py create mode 100644 opencompass/openicl/icl_evaluator/code_evaluator.py diff --git a/dataset-index.yml b/dataset-index.yml index e998f65f..dc50f396 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -529,6 +529,11 @@ category: Understanding paper: https://proceedings.neurips.cc/paper_files/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf configpath: opencompass/configs/datasets/SuperGLUE_MultiRC +- multipl_e: + name: MultiPL-E + category: Code + paper: https://arxiv.org/pdf/2210.14868 + configpath: opencompass/configs/datasets/multipl_e - narrativeqa: name: NarrativeQA category: Understanding diff --git a/opencompass/configs/datasets/multipl_e/multiple_top_ten_gen.py b/opencompass/configs/datasets/multipl_e/multiple_top_ten_gen.py new file mode 100644 index 00000000..93ab2962 --- /dev/null +++ b/opencompass/configs/datasets/multipl_e/multiple_top_ten_gen.py @@ -0,0 +1,56 @@ +# Select the 10 most popular programming languages from MultiPL-E to compose the test set. + +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 MultiplEDataset, MultiplEEvaluator + + +_TOP_TEN_LANGUAGE_ = ['cpp', 'cs', 'go', 'java', 'rb', 'js', 'php', 'r', 'rs', 'sh'] + +multiple_reader_cfg = dict(input_columns=['language', 'prompt'], output_column='tests') + +multiple_infer_cfg = dict( + prompt_template=dict(type=PromptTemplate, template='Based on the provided {language} code snippet, complete the subsequent content. The initial part of the completed code must match the provided code snippet exactly:\n{prompt}'), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +multiple_eval_cfg = { + lang: dict( + evaluator=dict( + type=MultiplEEvaluator, + language=lang, + ip_address='https://opencompass-multiple-evaluator.hf.space', + ), + pred_role='BOT', + ) for lang in _TOP_TEN_LANGUAGE_ +} + +multiple_datasets = [ + dict( + type=MultiplEDataset, + abbr=f'humaneval-multiple-{lang}', + language=lang, + num_repeats=1, + path='opencompass/multipl_e', + tag='humaneval', + reader_cfg=multiple_reader_cfg, + infer_cfg=multiple_infer_cfg, + eval_cfg=multiple_eval_cfg[lang], + ) for lang in _TOP_TEN_LANGUAGE_ +] + +multiple_datasets += [ + dict( + type=MultiplEDataset, + abbr=f'mbpp-multiple-{lang}', + language=lang, + num_repeats=1, + path='opencompass/multipl_e', + tag='mbpp', + reader_cfg=multiple_reader_cfg, + infer_cfg=multiple_infer_cfg, + eval_cfg=multiple_eval_cfg[lang], + ) for lang in _TOP_TEN_LANGUAGE_ +] diff --git a/opencompass/configs/models/phi/hf_phi_4.py b/opencompass/configs/models/phi/hf_phi_4.py new file mode 100644 index 00000000..1f4f6754 --- /dev/null +++ b/opencompass/configs/models/phi/hf_phi_4.py @@ -0,0 +1,12 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='phi-4', + path='microsoft/phi-4', + max_out_len=1024, + batch_size=8, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/datasets/__init__.py b/opencompass/datasets/__init__.py index 6d135f61..49cd1522 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -98,6 +98,7 @@ from .mmlu_cf import * # noqa: F401, F403 from .mmlu_pro import * # noqa: F401, F403 from .MMLUArabic import * # noqa: F401, F403 from .mmmlu import * # noqa: F401, F403 +from .multipl_e import * # noqa: F401, F403 from .multirc import * # noqa: F401, F403 from .musr import * # noqa: F401, F403 from .narrativeqa import * # noqa: F401, F403 diff --git a/opencompass/datasets/custom.py b/opencompass/datasets/custom.py index 110cb72b..b5eb8dbb 100644 --- a/opencompass/datasets/custom.py +++ b/opencompass/datasets/custom.py @@ -183,6 +183,33 @@ class CustomDataset(BaseDataset): return Dataset.from_list(data) +@LOAD_DATASET.register_module() +class CodeCustomDataset(BaseDataset): + + @staticmethod + def load(path, file_name=None, local_mode=False, num_repeats=1, **kwargs): + path = get_data_path(path, local_mode=local_mode) + if file_name is not None: + path = os.path.join(path, file_name) + data = [] + if path.endswith('.jsonl'): + with open(path, 'r', encoding='utf-8') as f: + for line in f: + data.extend( + [json.loads(line.strip()) for _ in range(num_repeats)]) + elif path.endswith('.csv'): + with open(path, 'r', encoding='utf-8-sig') as f: + reader = csv.reader(f) + header = next(reader) + for row in reader: + data.extend( + [dict(zip(header, row)) for _ in range(num_repeats)]) + else: + raise ValueError(f'Unsupported file format: {path}') + + return Dataset.from_list(data) + + class CircularCustomDataset(CustomDataset, metaclass=CircularDatasetMeta): dataset_class = CustomDataset diff --git a/opencompass/datasets/multipl_e.py b/opencompass/datasets/multipl_e.py new file mode 100644 index 00000000..657b52de --- /dev/null +++ b/opencompass/datasets/multipl_e.py @@ -0,0 +1,103 @@ +import json +import os.path as osp + +from datasets import Dataset + +from opencompass.openicl.icl_evaluator.code_evaluator import CodeEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils import get_data_path + +from .base import BaseDataset + +# currently supporting languages +_HUMANEVAL_LANGUAGE_ = [ + 'adb', 'clj', 'cpp', 'cs', 'd', 'dart', 'elixir', 'go', 'hs', 'java', 'jl', + 'js', 'lua', 'ml', 'php', 'pl', 'py', 'r', 'rb', 'rkt', 'rs', 'scala', + 'sh', 'swift', 'ts' +] +_MBPP_LANGUAGE_ = [ + 'adb', 'clj', 'cpp', 'cs', 'd', 'elixir', 'go', 'hs', 'java', 'jl', 'js', + 'lua', 'ml', 'php', 'pl', 'py', 'r', 'rb', 'rkt', 'rs', 'scala', 'sh', + 'swift', 'ts' +] + + +@LOAD_DATASET.register_module() +class MultiplEDataset(BaseDataset): + + @staticmethod + def load(path: str, + language: str, + num_repeats: int = 1, + tag: str = 'humaneval', + local_mode: bool = False): + """Load dataset for pass k mode. + + Args: + path(str): The path to the dataset. + language(str): The language of the dataset. + num_repeats(int): Number of repetition for this dataset to get. + tag(str): The tag of the dataset. + local_mode(bool): Whether to load the dataset in local mode. + + Returns: + Dataset: A PyTorch dataset. + """ + path = get_data_path(path, local_mode=local_mode) + assert tag in ['humaneval', + 'mbpp'], 'tag must be in ["humaneval", "mbpp"]' + if tag == 'humaneval': + assert language in _HUMANEVAL_LANGUAGE_, ( + f'language must be in {_HUMANEVAL_LANGUAGE_}') + else: + assert language in _MBPP_LANGUAGE_, ( + f'language must be in {_MBPP_LANGUAGE_}') + file_path = osp.join(path, f'{tag}-{language}.jsonl') + dataset = [] + with open(file_path, 'r', encoding='utf-8') as f: + for line in f: + dataset.extend( + [json.loads(line.strip()) for _ in range(num_repeats)]) + return Dataset.from_list(dataset) + + +class MultiplEEvaluator(CodeEvaluator): + + def _stop_at_stop_token(self, decoded_string, stop_tokens): + """Produces the prefix of decoded_string that ends at the first + occurrence of a stop_token. + + WARNING: the decoded_string *must not* include the prompt, + which may have stop tokens itself. + + Args: + decoded_string: A string generated by the model. + stop_tokens: A list of strings, where each string is a stop token. + Returns: + The decoded_string, truncated at the first occurrence of a stop + token. + """ + min_stop_index = len(decoded_string) + for stop_token in stop_tokens: + stop_index = decoded_string.find(stop_token) + if stop_index != -1 and stop_index < min_stop_index: + min_stop_index = stop_index + return decoded_string[:min_stop_index] + + def _process_completions(self, test_case, completions): + """Process completions with a test case. + + Args: + test_case: A test case. + completions: A list of completions. + Returns: + A list of processed completions. + """ + processed_completions = [] + for comp in completions: + comp = self._extract_code(comp) + post_comp = self._remove_prefix(test_case['prompt'], comp) + post_comp = self._stop_at_stop_token(post_comp, + test_case['stop_tokens']) + processed_completions.append(post_comp) + return processed_completions diff --git a/opencompass/openicl/icl_evaluator/code_evaluator.py b/opencompass/openicl/icl_evaluator/code_evaluator.py new file mode 100644 index 00000000..d586cd6e --- /dev/null +++ b/opencompass/openicl/icl_evaluator/code_evaluator.py @@ -0,0 +1,267 @@ +# flake8: noqa: E501 + +import difflib +import os +import re +import tempfile +import time +from typing import Any, Dict, List, Optional, Tuple, Union + +from datasets import Dataset +from gradio_client import Client + +from opencompass.openicl.icl_evaluator import BaseEvaluator +from opencompass.registry import ICL_EVALUATORS + + +@ICL_EVALUATORS.register_module() +class CodeEvaluator(BaseEvaluator): + """Evaluator for code generation tasks. + + This evaluator sends code to a remote evaluation service to test its + functionality against provided test cases. It handles code extraction, + processing, and result analysis. + """ + + def __init__(self, + language: str, + ip_address: str = 'localhost', + retry: int = 3) -> None: + """Initialize the CodeEvaluator. + + Args: + language (str): Programming language of the code to evaluate. + ip_address (str, optional): IP address of the evaluation service. Defaults to 'localhost'. + retry (int, optional): Number of retry attempts for failed connections. Defaults to 3. + """ + self.language = language + self.retry = retry + self.client = Client(ip_address) + super().__init__() + + def _extract_code(self, text: str) -> str: + """Extract code from markdown-formatted text. + + Args: + text (str): Text that may contain code blocks in markdown format. + + Returns: + str: Extracted code from the last code block, or the original text if no code blocks found. + """ + blocks = re.findall(r'```\w*\n(.*?)```', text, re.DOTALL) + if len(blocks) >= 1: + text = blocks[0] + return text + + def _code_eval_service( + self, input_data: Union[Dict, List, + str]) -> Tuple[bool, Union[Dict, List, Any]]: + """Send code to the remote evaluation service using gradio_client and + get the results. + + Args: + input_data: Can be one of: + - dict: Dictionary containing code information for a single test case + - list: List of dictionaries for batch evaluation + - str: File path to code file + + Returns: + tuple: (succeed, output) + - succeed (bool): Whether the request was successful + - output (dict/list/str): Evaluation results or error message + """ + try: + temp_file_path = None + # Handle file path input + if isinstance(input_data, str): + with tempfile.NamedTemporaryFile(suffix=f'.{self.language}', + delete=False) as temp_file: + temp_file_path = temp_file.name + with open(input_data, 'r') as src_file: + content = src_file.read() + temp_file.write(content.encode()) + input_data = temp_file_path + + # Send to evaluation service + result = self.client.predict(input_data, api_name='/evaluate') + + # Process the result + if isinstance(result, (dict, list)): + return True, result + else: + # Try to parse the result as JSON if it's a string + try: + import json + parsed_result = json.loads(result) + return True, parsed_result + except: # noqa: E722 + return True, {'status': 'unknown', 'raw_result': result} + + except Exception as e: + return False, str(e) + finally: + # Clean up temporary file if it was created + if temp_file_path and os.path.exists(temp_file_path): + try: + os.unlink(temp_file_path) + except: # noqa: E722 + pass + + def _remove_prefix(self, + prompt: str, + completion: str, + threshold: float = 0.95) -> str: + """Determine the truncation point in the completion based on the last + line of the prompt, remove all content before that line in the + completion, and return the completion string after removing the prefix. + This is done to convert chatbot-style inference mode to completion + mode. + + Args: + prompt (str): The prompt text. + completion (str): The completion text. + threshold (float): Line similarity threshold. + + Returns: + str: The completion string after removing the prefix. + """ + prompt_lines = prompt.splitlines() + completion_lines = completion.splitlines() + + if not prompt_lines: + return completion + + last_prompt_line = prompt_lines[-1] + cut_index = -1 + + for i, completion_line in enumerate(completion_lines): + similarity = difflib.SequenceMatcher(None, last_prompt_line, + completion_line).ratio() + if similarity >= threshold: + cut_index = i + break + + if cut_index != -1: + return '\n'.join(completion_lines[cut_index + 1:]) + else: + return completion + + def _process_completions(self, test_case: dict, completions: list) -> list: + """Process code completion list, which typically involves extracting + code, removing repetitive prefixes caused by chatbot mode, and other + steps to ensure the model-generated code can be compiled successfully. + + Args: + test_case (dict): Dictionary containing test case information including: + completions (list): List of code completions generated by the model. + + Returns: + list: Processed code completion list. + """ + processed_completions = [] + for comp in completions: + comp = self._extract_code(comp) + post_comp = self._remove_prefix(test_case['prompt'], comp) + processed_completions.append(post_comp) + return processed_completions + + def _evaluate( + self, input_data: Union[Dict, List] + ) -> Tuple[bool, Optional[Union[Dict, List]], Optional[str]]: + """Evaluate code with retry mechanism. + + Args: + input_data: Can be either: + - dict: Dictionary containing code and test information for a single test case + - list: List of dictionaries for batch evaluation + + Returns: + tuple: (success, output, error_message) + - success (bool): Whether the evaluation was successful + - output (dict or list): Evaluation output (if successful) + - error_message (str): Error message (if failed) + """ + num_retry = 0 + while num_retry < self.retry: + succeed, output = self._code_eval_service(input_data) + if not succeed: + num_retry += 1 + time.sleep(10) + else: + break + + if not succeed: + return False, None, f'code eval service connection failed: {output}' + + return True, output, None + + def score(self, predictions: List, references: List, + test_set: Dataset) -> Dict: + """Score code generation predictions against references. + + Args: + predictions (list): List of model-generated code completions. + references (list): List of reference solutions (not directly used in evaluation). + test_set (Dataset): Dataset containing test cases and other metadata. + + Returns: + dict: Evaluation results including: + - accuracy: Percentage of correctly solved problems + - details: Detailed results for each test case + - error: Error message if evaluation failed + """ + if len(predictions) != len(references): + return { + 'error': + 'predictions and references have different ' + f'length. len(predictions): {len(predictions)}, ' + f'len(references): {len(references)}' + } + + test_set = test_set.to_pandas() + # Use the first column as the unique identifier + test_set_origin = test_set.drop_duplicates(subset=test_set.columns[0]) + num_repeats = int(len(test_set) / len(test_set_origin)) + + # 1. Prepare data for all test cases + all_test_cases = [] + for i in range(len(test_set_origin)): + test_case = test_set_origin.iloc[i] + completions = predictions[i * num_repeats:(i + 1) * num_repeats] + + # Process code completions + processed_completions = self._process_completions( + test_case, completions) + + result_dict = { + 'name': test_case['name'], + 'language': test_case['language'], + 'prompt': test_case['prompt'], + 'tests': test_case['tests'], + 'processed_completions': processed_completions, + 'completions': completions + } + + all_test_cases.append(result_dict) + + # 2. Send all test cases to the evaluation service + success, outputs, error_message = self._evaluate(all_test_cases) + if not success: + return {'error': error_message} + + # 3. Process the returned results + details = [] + correct = 0 + for output in outputs: + if output.get('status') == 'OK': + output['correct'] = True + correct += 1 + else: + output['correct'] = False + + details.append(output) + + return { + f'pass@{num_repeats}': 100 * correct / len(test_set_origin), + 'details': details + } diff --git a/opencompass/utils/datasets_info.py b/opencompass/utils/datasets_info.py index 5f055cc0..00db25e8 100644 --- a/opencompass/utils/datasets_info.py +++ b/opencompass/utils/datasets_info.py @@ -193,6 +193,12 @@ DATASETS_MAPPING = { "hf_id": "", "local": "./data/mmlu_pro", }, + # MultiPL-E + "opencompass/multipl_e": { + "ms_id": "", + "hf_id": "", + "local": "./data/multipl_e", + }, # NQ "opencompass/natural_question": { "ms_id": "opencompass/natural_question", @@ -627,6 +633,11 @@ DATASETS_URL = { "http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/mmlu_pro.zip", "md5": "e3200c7380f4cea5f13c768f2815fabb", }, + "multipl_e": { + "url": + "http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/multipl_e.zip", + "md5": "24462aac7a38a4a62f5c5e89eb614e20", + }, "/Longbench": { "url": "http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/Longbench.zip", From 64128916d0a180b63b634cc9e894ae907a605759 Mon Sep 17 00:00:00 2001 From: Linchen Xiao Date: Mon, 24 Mar 2025 11:21:14 +0800 Subject: [PATCH 2/6] [Update] Increase memory size for CPU job of VOLC Runner (#1962) * [Update] Increase memory size for CPU job of VOLC Runner * [Update] Increase memory size for CPU job of VOLC Runner --- opencompass/runners/volc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/opencompass/runners/volc.py b/opencompass/runners/volc.py index a01f7cac..37cd441b 100644 --- a/opencompass/runners/volc.py +++ b/opencompass/runners/volc.py @@ -256,7 +256,7 @@ class VOLCRunner(BaseRunner): with open(config_path) as fp: volc_cfg = yaml.safe_load(fp) if num_gpus <= 0: - flavor = 'ml.c3i.2xlarge' + flavor = 'ml.r3i.2xlarge' elif num_gpus == 1: flavor = 'ml.pni2l.3xlarge' elif num_gpus == 2: From aa059939225d6490ea3aa650237f1920a51648b8 Mon Sep 17 00:00:00 2001 From: Linchen Xiao Date: Mon, 24 Mar 2025 14:24:12 +0800 Subject: [PATCH 3/6] [Update] Add dataset configurations of no max_out_len (#1967) * [Update] Add dataset configurations of no max_out_len * update test torch version * update test torch version * update test torch version * update test torch version --- .github/workflows/pr-stage-check.yml | 6 +- .../arc_prize_public_evaluation_gen_fedd04.py | 56 ++++++ .../GaokaoBench_no_subjective_gen_d16acb.py | 45 +++++ .../MathBench/mathbench_2024_gen_4b8f28.py | 81 ++++++++ .../datasets/bbh/bbh_llmjudge_gen_b5bdf1.py | 189 ++++++++++++++++++ .../bigcodebench_hard_complete_gen_2888d3.py | 45 +++++ .../bigcodebench_hard_instruct_gen_c3d5ad.py | 45 +++++ .../datasets/cmo_fib/cmo_fib_gen_2783e5.py | 39 ++++ .../gsm8k/gsm8k_0shot_v2_gen_17d799.py | 37 ++++ .../korbench/korbench_llmjudge_gen_17854d.py | 117 +++++++++++ .../math/math_500_llmjudge_gen_6ff468.py | 96 +++++++++ .../datasets/nq/nq_open_1shot_gen_2e45e5.py | 2 +- .../datasets/scicode/scicode_gen_62c139.py | 29 +++ .../triviaqa_wiki_1shot_gen_c87d61.py | 62 ++++++ 14 files changed, 845 insertions(+), 4 deletions(-) create mode 100644 opencompass/configs/datasets/ARC_Prize_Public_Evaluation/arc_prize_public_evaluation_gen_fedd04.py create mode 100644 opencompass/configs/datasets/GaokaoBench/GaokaoBench_no_subjective_gen_d16acb.py create mode 100644 opencompass/configs/datasets/MathBench/mathbench_2024_gen_4b8f28.py create mode 100644 opencompass/configs/datasets/bbh/bbh_llmjudge_gen_b5bdf1.py create mode 100644 opencompass/configs/datasets/bigcodebench/bigcodebench_hard_complete_gen_2888d3.py create mode 100644 opencompass/configs/datasets/bigcodebench/bigcodebench_hard_instruct_gen_c3d5ad.py create mode 100644 opencompass/configs/datasets/cmo_fib/cmo_fib_gen_2783e5.py create mode 100644 opencompass/configs/datasets/gsm8k/gsm8k_0shot_v2_gen_17d799.py create mode 100644 opencompass/configs/datasets/korbench/korbench_llmjudge_gen_17854d.py create mode 100644 opencompass/configs/datasets/math/math_500_llmjudge_gen_6ff468.py create mode 100644 opencompass/configs/datasets/scicode/scicode_gen_62c139.py create mode 100644 opencompass/configs/datasets/triviaqa/triviaqa_wiki_1shot_gen_c87d61.py diff --git a/.github/workflows/pr-stage-check.yml b/.github/workflows/pr-stage-check.yml index 15669a3f..a9871887 100644 --- a/.github/workflows/pr-stage-check.yml +++ b/.github/workflows/pr-stage-check.yml @@ -20,7 +20,7 @@ jobs: matrix: python-version: ['3.10'] include: - - torch: 2.0.0 + - torch: 2.5.1 steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} @@ -30,7 +30,7 @@ jobs: - name: Upgrade pip run: python -m pip install --upgrade pip - name: Install PyTorch - run: pip install torch==${{matrix.torch}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html + run: pip install torch==${{matrix.torch}} -f https://download.pytorch.org/whl/cpu/torch_stable.html - name: Install system dependencies run: | sudo sed -i '$ a deb http://th.archive.ubuntu.com/ubuntu jammy main' /etc/apt/sources.list @@ -106,7 +106,7 @@ jobs: - name: Upgrade pip run: python -m pip install pip --upgrade - name: Install PyTorch - run: pip install torch==2.0.0+${{matrix.platform}} -f https://download.pytorch.org/whl/${{matrix.platform}}/torch_stable.html + run: pip install torch==2.5.1 -f https://download.pytorch.org/whl/cpu/torch_stable.html - name: Install opencompass dependencies run: | pip install -r requirements.txt diff --git a/opencompass/configs/datasets/ARC_Prize_Public_Evaluation/arc_prize_public_evaluation_gen_fedd04.py b/opencompass/configs/datasets/ARC_Prize_Public_Evaluation/arc_prize_public_evaluation_gen_fedd04.py new file mode 100644 index 00000000..536349e9 --- /dev/null +++ b/opencompass/configs/datasets/ARC_Prize_Public_Evaluation/arc_prize_public_evaluation_gen_fedd04.py @@ -0,0 +1,56 @@ +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.arc_prize_public_evaluation import ARCPrizeDataset, ARCPrizeEvaluator + + +# The system_prompt defines the initial instructions for the model, +# setting the context for solving ARC tasks. +system_prompt = '''You are a puzzle solving wizard. You are given a puzzle from the abstraction and reasoning corpus developed by Francois Chollet.''' + +# User message template is a template for creating user prompts. It includes placeholders for training data and test input data, +# guiding the model to learn the rule and apply it to solve the given puzzle. +user_message_template = '''Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input: +---------------------------------------- +{training_data} +---------------------------------------- +Now, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.: +---------------------------------------- +[{{'input': {input_test_data}, 'output': [[]]}}] +---------------------------------------- +What is the output grid? Only provide the output grid in the form as in the example input and output pairs. Do not provide any additional information:''' + + +arc_prize_public_evaluation_reader_cfg = dict( + input_columns=['training_data', 'input_test_data'], + output_column='output_test_data' +) + +arc_prize_public_evaluation_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='SYSTEM',fallback_role='HUMAN', prompt=system_prompt), + dict(role='HUMAN', prompt=user_message_template), + ], + ) + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer) +) + +arc_prize_public_evaluation_eval_cfg = dict( + evaluator=dict(type=ARCPrizeEvaluator) +) + +arc_prize_public_evaluation_datasets = [ + dict( + abbr='ARC_Prize_Public_Evaluation', + type=ARCPrizeDataset, + path='opencompass/arc_prize_public_evaluation', + reader_cfg=arc_prize_public_evaluation_reader_cfg, + infer_cfg=arc_prize_public_evaluation_infer_cfg, + eval_cfg=arc_prize_public_evaluation_eval_cfg + ) +] \ No newline at end of file diff --git a/opencompass/configs/datasets/GaokaoBench/GaokaoBench_no_subjective_gen_d16acb.py b/opencompass/configs/datasets/GaokaoBench/GaokaoBench_no_subjective_gen_d16acb.py new file mode 100644 index 00000000..9ee5c917 --- /dev/null +++ b/opencompass/configs/datasets/GaokaoBench/GaokaoBench_no_subjective_gen_d16acb.py @@ -0,0 +1,45 @@ +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.datasets import GaokaoBenchDataset +from mmengine.config import read_base + +with read_base(): + from .GaokaoBench_prompts import MCQ_prompts, FBQ_prompts + +GaokaoBench_datasets = [] +for folder, prompts in [ + ('Multiple-choice_Questions', MCQ_prompts), + ('Fill-in-the-blank_Questions', FBQ_prompts), +]: + for p in prompts: + reader_cfg = { + 'input_columns': ['question'], + 'output_column': 'answer', + } + infer_cfg = { + 'ice_template': { + 'type': PromptTemplate, + 'template': {'round': [{'role': 'HUMAN', 'prompt': p['prefix_prompt'] + '{question}'}]}, + 'ice_token': '', + }, + 'retriever': {'type': ZeroRetriever}, + 'inferencer': {'type': GenInferencer}, + } + eval_cfg = { + 'evaluator': {'type': 'GaokaoBenchEvaluator' + '_' + p['type']}, + 'pred_role': 'BOT', + } + _base_path = 'opencompass/GAOKAO-BENCH' + dataset = { + 'type': GaokaoBenchDataset, + 'abbr': 'GaokaoBench_' + p['keyword'], + 'path': _base_path, + 'filename': '/' + folder + '/' + p['keyword'] + '.json', + 'name': p['keyword'], + 'reader_cfg': reader_cfg, + 'infer_cfg': infer_cfg, + 'eval_cfg': eval_cfg, + } + GaokaoBench_datasets.append(dataset) diff --git a/opencompass/configs/datasets/MathBench/mathbench_2024_gen_4b8f28.py b/opencompass/configs/datasets/MathBench/mathbench_2024_gen_4b8f28.py new file mode 100644 index 00000000..c3183716 --- /dev/null +++ b/opencompass/configs/datasets/MathBench/mathbench_2024_gen_4b8f28.py @@ -0,0 +1,81 @@ +from mmengine.config import read_base +from copy import deepcopy +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer, PPLInferencer +from opencompass.openicl.icl_evaluator import CircularEvaluator, AccEvaluator +from opencompass.datasets import MathBenchDataset, math_postprocess_v2 +from opencompass.utils.text_postprocessors import first_option_postprocess + +with read_base(): + from .mathbench_prompt import zero_shot_prompts, few_shot_prompts, mathbench_sets + +# Max for this dataset is 4 +num_shot = 0 +# Generate reasoning path or not, only for single choice +with_reasoning = True +# Use circular evaluation or not +with_circular_eval = True +# Use PPL mode in single choice test or not +use_ppl_single_choice = False + +assert 0 <= num_shot <= 4 +if num_shot == 0: + prompts = zero_shot_prompts +else: + prompts = {name: p[- 2 * num_shot - 2:] for name, p in few_shot_prompts.items()} + +mathbench_datasets = [] +for _split in mathbench_sets: + for _name in mathbench_sets[_split]: + if 'single_choice' in _name: + if with_reasoning: + template_round = prompts[_name + '_with_reasoning'] + else: + template_round = prompts[_name] + else: + template_round = prompts[_name] + + if 'single_choice' in _name: + pred_postprocessor = dict(type=first_option_postprocess, options='ABCD') + else: + pred_postprocessor = dict(type=math_postprocess_v2) + + if 'single_choice' in _name and with_circular_eval: + evaluator = dict(type=CircularEvaluator) + else: + evaluator = dict(type=AccEvaluator) + + # assemble the final config + mathbench_reader_cfg = dict(input_columns=['question'], output_column='answer') + if use_ppl_single_choice and 'single_choice' in _name and not with_reasoning: + template = {} + for answer in ['A', 'B', 'C', 'D']: + one_template_round = deepcopy(template_round) + one_template_round['round'][-1]['prompt'] = one_template_round['round'][-1]['prompt'].format(answer=answer) + template[answer] = dict(round=one_template_round) + mathbench_infer_cfg = dict( + prompt_template=dict(type=PromptTemplate, template=template), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=PPLInferencer), + ) + else: + mathbench_infer_cfg = dict( + prompt_template=dict(type=PromptTemplate, template=dict(round=template_round)), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), + ) + mathbench_eval_cfg = dict(evaluator=evaluator, pred_postprocessor=pred_postprocessor) + + mathbench_datasets.append( + dict( + abbr='mathbench-' + _split + '-' + _name, + type=MathBenchDataset, + path=f'data/mathbench_v1/{_split}', + name=_name, + with_circular=with_circular_eval, + reader_cfg=mathbench_reader_cfg, + infer_cfg=mathbench_infer_cfg, + eval_cfg=mathbench_eval_cfg, + ) + ) diff --git a/opencompass/configs/datasets/bbh/bbh_llmjudge_gen_b5bdf1.py b/opencompass/configs/datasets/bbh/bbh_llmjudge_gen_b5bdf1.py new file mode 100644 index 00000000..00426660 --- /dev/null +++ b/opencompass/configs/datasets/bbh/bbh_llmjudge_gen_b5bdf1.py @@ -0,0 +1,189 @@ +# flake8: noqa + +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.evaluator import GenericLLMEvaluator +from opencompass.datasets import BBHDataset +from opencompass.datasets.generic import generic_llmjudge_academic_postprocess + + +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', +] + + +GRADER_TEMPLATE = """ + Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. + + Here are some evaluation criteria: + 1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. + 2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. + 3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. + 4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. + 5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer. + + Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: + A: CORRECT + B: INCORRECT + Just return the letters "A" or "B", with no text around it. + + Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. + + + : \n{input}\n\n\n + : \n{target}\n\n\n + : \n{prediction}\n\n\n + + Judging the correctness of candidates' answers: +""".strip() + + +bbh_sets = bbh_multiple_choice_sets + bbh_free_form_sets + +# For zero shot inference in bbh +bbh_datasets = [] +for _name in bbh_sets: + bbh_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict(round=[ + dict( + role='HUMAN', + prompt=f"Question: {{input}}\n You must give your final answer by starting with 'So the answer is' " + ) + ])), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer)) + + bbh_eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.") + ], + round=[ + dict( + role='HUMAN', + prompt=GRADER_TEMPLATE + ), + ]), + ), + dataset_cfg=dict( + type=BBHDataset, + name=_name, + path='opencompass/bbh', + reader_cfg=bbh_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_academic_postprocess, metric_name='score'), + ), + 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()) + ) + + +# For original 3 shot inference in bbh +bbh_3_shot_datasets = [] +for _name in bbh_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)) + + bbh_eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.") + ], + round=[ + dict( + role='HUMAN', + prompt=GRADER_TEMPLATE + ), + ]), + ), + dataset_cfg=dict( + type=BBHDataset, + name=_name, + path='opencompass/bbh', + reader_cfg=bbh_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_academic_postprocess, metric_name='score'), + ), + pred_role='BOT', + ) + + bbh_3_shot_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())) diff --git a/opencompass/configs/datasets/bigcodebench/bigcodebench_hard_complete_gen_2888d3.py b/opencompass/configs/datasets/bigcodebench/bigcodebench_hard_complete_gen_2888d3.py new file mode 100644 index 00000000..e4c663fc --- /dev/null +++ b/opencompass/configs/datasets/bigcodebench/bigcodebench_hard_complete_gen_2888d3.py @@ -0,0 +1,45 @@ +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)) + +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/', + remote_execute_api= + 'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501 + 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', + ) +] diff --git a/opencompass/configs/datasets/bigcodebench/bigcodebench_hard_instruct_gen_c3d5ad.py b/opencompass/configs/datasets/bigcodebench/bigcodebench_hard_instruct_gen_c3d5ad.py new file mode 100644 index 00000000..b8dcc8ed --- /dev/null +++ b/opencompass/configs/datasets/bigcodebench/bigcodebench_hard_instruct_gen_c3d5ad.py @@ -0,0 +1,45 @@ +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)) + +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/', + remote_execute_api= + 'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501 + 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', + ) +] diff --git a/opencompass/configs/datasets/cmo_fib/cmo_fib_gen_2783e5.py b/opencompass/configs/datasets/cmo_fib/cmo_fib_gen_2783e5.py new file mode 100644 index 00000000..6fc1147c --- /dev/null +++ b/opencompass/configs/datasets/cmo_fib/cmo_fib_gen_2783e5.py @@ -0,0 +1,39 @@ +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 CMOFibDataset, MATHEvaluator, math_postprocess_v2 + + +cmo_fib_reader_cfg = dict( + input_columns=['question'], + output_column='answer' +) + + +cmo_fib_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt='{question}\n请一步一步地推理，并将最终答案写入\\boxed{}.'), + ], + ) + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer) +) + +cmo_fib_eval_cfg = dict( + evaluator=dict(type=MATHEvaluator, version='v2'), pred_postprocessor=dict(type=math_postprocess_v2) +) + +cmo_fib_datasets = [ + dict( + abbr='cmo_fib', + type=CMOFibDataset, + path='opencompass/cmo_fib', + reader_cfg=cmo_fib_reader_cfg, + infer_cfg=cmo_fib_infer_cfg, + eval_cfg=cmo_fib_eval_cfg + ) +] \ No newline at end of file diff --git a/opencompass/configs/datasets/gsm8k/gsm8k_0shot_v2_gen_17d799.py b/opencompass/configs/datasets/gsm8k/gsm8k_0shot_v2_gen_17d799.py new file mode 100644 index 00000000..43b38546 --- /dev/null +++ b/opencompass/configs/datasets/gsm8k/gsm8k_0shot_v2_gen_17d799.py @@ -0,0 +1,37 @@ +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 GSM8KDataset, gsm8k_postprocess, gsm8k_dataset_postprocess, Gsm8kEvaluator +from opencompass.datasets import MATHEvaluator, math_postprocess_v2 + +gsm8k_reader_cfg = dict(input_columns=['question'], output_column='answer') + +gsm8k_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt='{question}\nPlease reason step by step, and put your final answer within \\boxed{}.'), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +gsm8k_eval_cfg = dict( + evaluator=dict(type=MATHEvaluator, version='v2'), + pred_postprocessor=dict(type=math_postprocess_v2), + dataset_postprocessor=dict(type=gsm8k_dataset_postprocess), +) + +gsm8k_datasets = [ + dict( + abbr='gsm8k', + type=GSM8KDataset, + path='opencompass/gsm8k', + reader_cfg=gsm8k_reader_cfg, + infer_cfg=gsm8k_infer_cfg, + eval_cfg=gsm8k_eval_cfg, + ) +] diff --git a/opencompass/configs/datasets/korbench/korbench_llmjudge_gen_17854d.py b/opencompass/configs/datasets/korbench/korbench_llmjudge_gen_17854d.py new file mode 100644 index 00000000..a9cb644b --- /dev/null +++ b/opencompass/configs/datasets/korbench/korbench_llmjudge_gen_17854d.py @@ -0,0 +1,117 @@ +from opencompass.datasets.korbench.korbench import korbenchDataset, korbenchEvaluator +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.evaluator import GenericLLMEvaluator +from opencompass.datasets import generic_llmjudge_postprocess + +categories = ['cipher', 'counterfactual', 'logic', 'operation', 'puzzle'] + + +GRADER_TEMPLATE = """ + Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. + + Here are some evaluation criteria: + 1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. + 2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. + 3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. + 4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. + 5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer. + + Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: + A: CORRECT + B: INCORRECT + Just return the letters "A" or "B", with no text around it. + + Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. + + + : \n{prompt}\n\n\n + : \n{answer}\n\n\n + : \n{prediction}\n\n\n + + Judging the correctness of candidates' answers: +""".strip() + +korbench_0shot_single_datasets = [] + +for category in categories: + # Prompt template + prompt_template = dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='HUMAN', + prompt='' + ) + ], + round=[ + dict( + role='HUMAN', + prompt='{prompt}' # f-string + ) + ] + ) + ) + + # Reader configuration + reader_cfg = dict( + input_columns=['prompt'], + output_column='answer', + ) + + # Inference configuration + infer_cfg = dict( + prompt_template=prompt_template, + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer, max_out_len=1024), + ) + + # Evaluation configuration + eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.") + ], + round=[ + dict( + role='HUMAN', + prompt = GRADER_TEMPLATE + ), + ]), + ), + dataset_cfg=dict( + type=korbenchDataset, + path='opencompass/korbench', + prompt_mode='0_shot', + category=category, + reader_cfg=reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), + pred_role='BOT', + ) + + # Dataset + korbench_dataset = dict( + type=korbenchDataset, + abbr=f'korbench_{category}', + path='opencompass/korbench', + prompt_mode='0_shot', + category=category, + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg, + mode='singlescore', + ) + + korbench_0shot_single_datasets.append(korbench_dataset) diff --git a/opencompass/configs/datasets/math/math_500_llmjudge_gen_6ff468.py b/opencompass/configs/datasets/math/math_500_llmjudge_gen_6ff468.py new file mode 100644 index 00000000..67d74266 --- /dev/null +++ b/opencompass/configs/datasets/math/math_500_llmjudge_gen_6ff468.py @@ -0,0 +1,96 @@ +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.evaluator import GenericLLMEvaluator +from opencompass.datasets import generic_llmjudge_postprocess +from opencompass.datasets import MATHDataset + + +# ----------------------------- Detailed Config ----------------------------- + +math_reader_cfg = dict(input_columns=['problem'], output_column='solution') + +math_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt='{problem}\nRemember to put your final answer within \\boxed{}.'), + ] + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + + +GRADER_TEMPLATE = """ + Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. + + Here are some evaluation criteria: + 1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. + 2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. + 3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. + 4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. + 5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer. + + Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: + A: CORRECT + B: INCORRECT + Just return the letters "A" or "B", with no text around it. + + Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. + + + : \n{problem}\n\n\n + : \n{solution}\n\n\n + : \n{prediction}\n\n\n + + Judging the correctness of candidates' answers: +""".strip() + +# Evaluation configuration +math_eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.") + ], + round=[ + dict( + role='HUMAN', + prompt = GRADER_TEMPLATE + ), + ]), + ), + dataset_cfg=dict( + type=MATHDataset, + path='opencompass/math', + file_name = 'test_prm800k_500.json', + reader_cfg=math_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), + pred_role='BOT', +) + + +math_datasets = [ + dict( + type=MATHDataset, + abbr='math_prm800k_500-llmjudge', + path='opencompass/math', + file_name = 'test_prm800k_500.json', + reader_cfg=math_reader_cfg, + infer_cfg=math_infer_cfg, + eval_cfg=math_eval_cfg, + mode='singlescore', + ) +] diff --git a/opencompass/configs/datasets/nq/nq_open_1shot_gen_2e45e5.py b/opencompass/configs/datasets/nq/nq_open_1shot_gen_2e45e5.py index e877b397..2155c404 100644 --- a/opencompass/configs/datasets/nq/nq_open_1shot_gen_2e45e5.py +++ b/opencompass/configs/datasets/nq/nq_open_1shot_gen_2e45e5.py @@ -20,7 +20,7 @@ for k in [1]: ) ), retriever=dict(type=ZeroRetriever), - inferencer=dict(type=GenInferencer, max_out_len=50) + inferencer=dict(type=GenInferencer) ) else: nq_infer_cfg = dict( diff --git a/opencompass/configs/datasets/scicode/scicode_gen_62c139.py b/opencompass/configs/datasets/scicode/scicode_gen_62c139.py new file mode 100644 index 00000000..9e1842c3 --- /dev/null +++ b/opencompass/configs/datasets/scicode/scicode_gen_62c139.py @@ -0,0 +1,29 @@ +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import ChatInferencer +from opencompass.datasets import SciCodeDataset, SciCodeEvaluator + + +SciCode_reader_cfg = dict(input_columns=['prompt'], output_column=None) + +SciCode_infer_cfg = dict( + ice_template=dict( + type=PromptTemplate, + template='', + ), + + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=ChatInferencer, infer_mode='every')) + +SciCode_eval_cfg = dict(evaluator=dict(type=SciCodeEvaluator, dataset_path='./data/scicode', with_bg=False)) + +SciCode_datasets = [ + dict( + abbr='SciCode', + type=SciCodeDataset, + path='./data/scicode', + with_bg=False, + reader_cfg=SciCode_reader_cfg, + infer_cfg=SciCode_infer_cfg, + eval_cfg=SciCode_eval_cfg) +] diff --git a/opencompass/configs/datasets/triviaqa/triviaqa_wiki_1shot_gen_c87d61.py b/opencompass/configs/datasets/triviaqa/triviaqa_wiki_1shot_gen_c87d61.py new file mode 100644 index 00000000..27853fa4 --- /dev/null +++ b/opencompass/configs/datasets/triviaqa/triviaqa_wiki_1shot_gen_c87d61.py @@ -0,0 +1,62 @@ +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever, FixKRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.datasets import TriviaQADatasetV2, TriviaQAEvaluator + + +triviaqa_datasets = [] +for k in [1]: + triviaqa_reader_cfg = dict( + input_columns=['question'], output_column='answer', train_split='train', test_split='validation') + + if k == 0: + triviaqa_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt='Q: {question}'), + dict(role='BOT', prompt='A:'), + ] + ) + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer) + ) + else: + triviaqa_infer_cfg = dict( + ice_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt='Q: {question}'), + dict(role='BOT', prompt='A: {answer}.\n'), + ] + ), + ), + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin='', + round=[ + dict(role='HUMAN', prompt='Q: {question}'), + dict(role='BOT', prompt='A:'), + ] + ), + ice_token='', + ), + retriever=dict(type=FixKRetriever, fix_id_list=list(range(k))), + inferencer=dict(type=GenInferencer), + ) + + triviaqa_eval_cfg = dict(evaluator=dict(type=TriviaQAEvaluator), pred_role='BOT') + + triviaqa_datasets.append( + dict( + type=TriviaQADatasetV2, + abbr=f'triviaqa_wiki_{k}shot', + path='opencompass/trivia_qa', + reader_cfg=triviaqa_reader_cfg, + infer_cfg=triviaqa_infer_cfg, + eval_cfg=triviaqa_eval_cfg) + ) From db96161a4eeb0fc5be9b174d05e443215b6a0879 Mon Sep 17 00:00:00 2001 From: Linchen Xiao Date: Mon, 24 Mar 2025 14:25:12 +0800 Subject: [PATCH 4/6] [Update] Add SuperGPQA subset metrics (#1966) --- .../supergpqa_llmjudge_gen_12b8bc.py | 4 +- opencompass/datasets/supergpqa/supergpqa.py | 132 ++++++++++++++++++ .../evaluator/generic_llm_evaluator.py | 17 ++- .../icl_evaluator/icl_base_evaluator.py | 8 ++ 4 files changed, 156 insertions(+), 5 deletions(-) diff --git a/opencompass/configs/datasets/supergpqa/supergpqa_llmjudge_gen_12b8bc.py b/opencompass/configs/datasets/supergpqa/supergpqa_llmjudge_gen_12b8bc.py index 02e6f2da..053eda07 100644 --- a/opencompass/configs/datasets/supergpqa/supergpqa_llmjudge_gen_12b8bc.py +++ b/opencompass/configs/datasets/supergpqa/supergpqa_llmjudge_gen_12b8bc.py @@ -1,5 +1,5 @@ from opencompass.datasets.supergpqa.supergpqa import ( - SuperGPQADataset, + SuperGPQADataset, supergpqa_llmjudge_postprocess ) from opencompass.openicl.icl_inferencer import GenInferencer from opencompass.openicl.icl_prompt_template import PromptTemplate @@ -87,7 +87,7 @@ eval_cfg = dict( reader_cfg=reader_cfg, ), judge_cfg=dict(), - dict_postprocessor=dict(type=generic_llmjudge_postprocess), + dict_postprocessor=dict(type=supergpqa_llmjudge_postprocess), ), ) supergpqa_dataset = dict( diff --git a/opencompass/datasets/supergpqa/supergpqa.py b/opencompass/datasets/supergpqa/supergpqa.py index 9dd96dd4..401422e1 100644 --- a/opencompass/datasets/supergpqa/supergpqa.py +++ b/opencompass/datasets/supergpqa/supergpqa.py @@ -1,4 +1,5 @@ import os +import re from datasets import Dataset, load_dataset @@ -7,6 +8,7 @@ from opencompass.datasets.supergpqa.supergpqa_eval import ( from opencompass.datasets.supergpqa.supergpqa_utils import load_yaml from opencompass.openicl.icl_evaluator import BaseEvaluator from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET +from opencompass.utils import get_logger from ..base import BaseDataset @@ -180,3 +182,133 @@ class SuperGPQAEvaluator(BaseEvaluator): 'details': details, } + + +def _generic_llmjudge_postprocess(judgement: str): + match = re.search(r'(A|B)', judgement) + grade_letter = (match.group(0) if match else 'B' + ) # Default to "INCORRECT" if no match + return grade_letter + + +def supergpqa_llmjudge_postprocess( + output: dict, + output_path: str, + dataset: Dataset, +) -> dict: + # Get the original dataset + original_dataset = dataset.reader.dataset['test'] + + judged_answers = [] + original_responses = [] + references = [] + details = [] + + # Initialize statistics dictionaries + stats = {'discipline': {}, 'field': {}, 'subfield': {}} + + total_correct = 0 + total_count = 0 + + # Process each sample + for k, v in output.items(): + idx = int(k) # Convert key to integer for indexing + original_responses.append(v['prediction']) + processed_judge = _generic_llmjudge_postprocess(v['prediction']) + + # Get category information from the dataset + sample = original_dataset[idx] + discipline = sample.get('discipline', 'unknown') + field = sample.get('field', 'unknown') + subfield = sample.get('subfield', 'unknown') + + # Initialize category stats if not exists + for level, key in [ + ('discipline', discipline), + ('field', f'{discipline}/{field}'), + ('subfield', f'{discipline}/{field}/{subfield}'), + ]: + if key not in stats[level]: + stats[level][key] = {'correct': 0, 'total': 0} + + # Record the judgment + if processed_judge is not None: + judged_answers.append(processed_judge) + try: + gold = v['gold'] + references.append(gold) + except KeyError: + get_logger().warning( + f'No gold answer for {k}, use empty string as reference!') + gold = '' + references.append('') + + # Check if the answer is correct (A means correct) + is_correct = processed_judge == 'A' + total_count += 1 + + if is_correct: + total_correct += 1 + # Update category stats + for level, key in [ + ('discipline', discipline), + ('field', f'{discipline}/{field}'), + ('subfield', f'{discipline}/{field}/{subfield}'), + ]: + stats[level][key]['correct'] += 1 + + # Update category totals + for level, key in [ + ('discipline', discipline), + ('field', f'{discipline}/{field}'), + ('subfield', f'{discipline}/{field}/{subfield}'), + ]: + stats[level][key]['total'] += 1 + # Add to details + details.append({ + 'id': k, + 'question': sample['question'], + 'options': sample['options'], + 'origin_prompt': v['origin_prompt'], + 'llm_judge': processed_judge, + 'gold': gold, + 'is_correct': is_correct, + 'discipline': discipline, + 'field': field, + 'subfield': subfield, + }) + + # Calculate overall accuracy with two decimal places + overall_accuracy = (round( + (total_correct / total_count * 100), 2) if total_count > 0 else 0.00) + + # Initialize results dictionary + results = { + 'accuracy': overall_accuracy, + 'total_correct': total_correct, + 'total_count': total_count, + 'details': details, + } + + # Calculate accuracy for each category and flatten into results + for level in stats: + for key, value in stats[level].items(): + if value['total'] > 0: + # Calculate accuracy with two decimal places + accuracy = round((value['correct'] / value['total'] * 100), 2) + + # Create a flattened key for the category + flat_key = f'SuperGPQA-{level}' + if level == 'discipline': + flat_key = f'SuperGPQA-{key}' + elif level == 'field': + discipline, field = key.split('/') + flat_key = f'SuperGPQA-{discipline}-{field}' + elif level == 'subfield': + discipline, field, subfield = key.split('/') + flat_key = f'SuperGPQA-{discipline}-{field}-{subfield}' + + # Add to results + results[flat_key] = accuracy + + return results diff --git a/opencompass/evaluator/generic_llm_evaluator.py b/opencompass/evaluator/generic_llm_evaluator.py index c0b33a69..2b829ba1 100644 --- a/opencompass/evaluator/generic_llm_evaluator.py +++ b/opencompass/evaluator/generic_llm_evaluator.py @@ -84,6 +84,8 @@ class GenericLLMEvaluator(BaseEvaluator): references: Optional[List] = None, ) -> Dict: """Apply to single-model scoring.""" + assert len(predictions) == len( + references), 'predictions and references must have the same length' # -------------- Build Inferencer ---------------- self.build_inferencer() @@ -127,7 +129,7 @@ class GenericLLMEvaluator(BaseEvaluator): prompt_template=self.prompt_template) output = mmengine.load(self.output_path) - return self.output_postprocess(output) + return self.output_postprocess(output, dataset) def pred_postprocess(self, predictions: List) -> Dict: if self.pred_postprocessor is None: @@ -137,15 +139,24 @@ class GenericLLMEvaluator(BaseEvaluator): proc = TEXT_POSTPROCESSORS.get(kwargs.pop('type')) return [proc(pred, **kwargs) for pred in predictions] - def output_postprocess(self, output: Dict) -> Dict: + def output_postprocess(self, output: Dict, dataset=None) -> Dict: """Postprocess output by adding necessary statistics or data into it.""" + import inspect + if self.dict_postprocessor is None: return output else: kwargs = self.dict_postprocessor proc = DICT_POSTPROCESSORS.get(kwargs.pop('type')) - return proc(output, self.output_path, **kwargs) + sig = inspect.signature(proc) + if 'dataset' in sig.parameters: + return proc(output, + self.output_path, + dataset=dataset, + **kwargs) + else: + return proc(output, self.output_path, **kwargs) @property def default_judge_cfg(self): diff --git a/opencompass/openicl/icl_evaluator/icl_base_evaluator.py b/opencompass/openicl/icl_evaluator/icl_base_evaluator.py index e2aad9be..794c0ed6 100644 --- a/opencompass/openicl/icl_evaluator/icl_base_evaluator.py +++ b/opencompass/openicl/icl_evaluator/icl_base_evaluator.py @@ -89,6 +89,14 @@ class BaseEvaluator: original_dataset: Dataset, **score_kwargs, ): + # Check if predictions and references have the + # same length if both are provided + if 'predictions' in score_kwargs and 'references' in score_kwargs: + if len(score_kwargs['predictions']) != len( + score_kwargs['references']): + raise ValueError( + 'Predictions and references must have the same length') + real_size = len(original_dataset) // n all_details = [] all_results = [] From 37307fa99671260a4f8e52b27c48219dedcd74fd Mon Sep 17 00:00:00 2001 From: Myhs_phz Date: Mon, 24 Mar 2025 14:51:39 +0800 Subject: [PATCH 5/6] [Update] Add QWQ32b model config (#1959) * feat qwq-32b * fix * feat phi_4 --------- Co-authored-by: Linchen Xiao --- .../configs/models/qwq/lmdeploy_qwq_32b.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 opencompass/configs/models/qwq/lmdeploy_qwq_32b.py diff --git a/opencompass/configs/models/qwq/lmdeploy_qwq_32b.py b/opencompass/configs/models/qwq/lmdeploy_qwq_32b.py new file mode 100644 index 00000000..6c2bf078 --- /dev/null +++ b/opencompass/configs/models/qwq/lmdeploy_qwq_32b.py @@ -0,0 +1,17 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='QwQ-32B', + path='Qwen/QwQ-32B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=2), + gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192), + max_seq_len=32768, + max_out_len=8192, + batch_size=16, + run_cfg=dict(num_gpus=2), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] \ No newline at end of file From 07930b854a51908dfe0ea00c24b705d4a10662fd Mon Sep 17 00:00:00 2001 From: Linchen Xiao Date: Mon, 24 Mar 2025 18:38:06 +0800 Subject: [PATCH 6/6] [Update] Add Korbench config with no max_out_len (#1968) * Add Korbench no max_out_len * Add Korbench no max_out_len --- .../korbench/korbench_llmjudge_gen_56cf43.py | 117 ++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 opencompass/configs/datasets/korbench/korbench_llmjudge_gen_56cf43.py diff --git a/opencompass/configs/datasets/korbench/korbench_llmjudge_gen_56cf43.py b/opencompass/configs/datasets/korbench/korbench_llmjudge_gen_56cf43.py new file mode 100644 index 00000000..8c5c0cd2 --- /dev/null +++ b/opencompass/configs/datasets/korbench/korbench_llmjudge_gen_56cf43.py @@ -0,0 +1,117 @@ +from opencompass.datasets.korbench.korbench import korbenchDataset, korbenchEvaluator +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.evaluator import GenericLLMEvaluator +from opencompass.datasets import generic_llmjudge_postprocess + +categories = ['cipher', 'counterfactual', 'logic', 'operation', 'puzzle'] + + +GRADER_TEMPLATE = """ + Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. + + Here are some evaluation criteria: + 1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. + 2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. + 3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. + 4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. + 5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer. + + Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: + A: CORRECT + B: INCORRECT + Just return the letters "A" or "B", with no text around it. + + Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. + + + : \n{prompt}\n\n\n + : \n{answer}\n\n\n + : \n{prediction}\n\n\n + + Judging the correctness of candidates' answers: +""".strip() + +korbench_0shot_single_datasets = [] + +for category in categories: + # Prompt template + prompt_template = dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='HUMAN', + prompt='' + ) + ], + round=[ + dict( + role='HUMAN', + prompt='{prompt}' # f-string + ) + ] + ) + ) + + # Reader configuration + reader_cfg = dict( + input_columns=['prompt'], + output_column='answer', + ) + + # Inference configuration + infer_cfg = dict( + prompt_template=prompt_template, + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), + ) + + # Evaluation configuration + eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.") + ], + round=[ + dict( + role='HUMAN', + prompt = GRADER_TEMPLATE + ), + ]), + ), + dataset_cfg=dict( + type=korbenchDataset, + path='opencompass/korbench', + prompt_mode='0_shot', + category=category, + reader_cfg=reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), + pred_role='BOT', + ) + + # Dataset + korbench_dataset = dict( + type=korbenchDataset, + abbr=f'korbench_{category}', + path='opencompass/korbench', + prompt_mode='0_shot', + category=category, + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg, + mode='singlescore', + ) + + korbench_0shot_single_datasets.append(korbench_dataset)