| | from .text_base import TextBaseDataset |
| | from .utils import build_judge, DEBUG_MESSAGE |
| | from ..smp import * |
| |
|
| |
|
| | class TextMCQDataset(TextBaseDataset): |
| | TYPE = 'MCQ' |
| |
|
| | DATASET_URL = {} |
| |
|
| | DATASET_MD5 = {} |
| |
|
| | def build_prompt(self, line): |
| |
|
| | if isinstance(line, int): |
| | line = self.data.iloc[line] |
| |
|
| | question = line['question'] |
| | options = { |
| | cand: line[cand] |
| | for cand in string.ascii_uppercase |
| | if cand in line and not pd.isna(line[cand]) |
| | } |
| | options_prompt = 'Options:\n' |
| | for key, item in options.items(): |
| | options_prompt += f'{key}. {item}\n' |
| | hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None |
| | prompt = '' |
| | if hint is not None: |
| | prompt += f'Hint: {hint}\n' |
| | prompt += f'Question: {question}\n' |
| | if len(options): |
| | prompt += options_prompt |
| | prompt += 'Please select the correct answer from the options above. \n' |
| |
|
| | msgs = [] |
| |
|
| | msgs.append(dict(type='text', value=prompt)) |
| |
|
| | return msgs |
| |
|
| | def evaluate(self, eval_file, **judge_kwargs): |
| | from .utils.multiple_choice import report_acc, report_acc_MMT, mcq_circular_eval, mcq_vanilla_eval |
| | |
| | dataset_map = { |
| | 'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_EN_V11': 'MMBench_V11', |
| | 'MMBench_TEST_CN': 'MMBench_CN', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11' |
| | } |
| | dataset = self.dataset_name |
| | if dataset in dataset_map: |
| | dataset = dataset_map[dataset] |
| | nproc = judge_kwargs.pop('nproc', 4) |
| |
|
| | circular = False |
| |
|
| | suffix = eval_file.split('.')[-1] |
| | model = judge_kwargs.get('model', 'exact_matching') |
| | assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] |
| | name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'} |
| | name_str = name_str_map[model] if model in name_str_map else model |
| |
|
| | if model == 'exact_matching': |
| | model = None |
| | elif gpt_key_set(): |
| | model = build_judge(**judge_kwargs) |
| | if not model.working(): |
| | warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') |
| | warnings.warn(DEBUG_MESSAGE) |
| | model = None |
| | else: |
| | warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') |
| | model = None |
| |
|
| | result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') |
| |
|
| | data = load(eval_file) |
| | data = data.sort_values(by='index') |
| | data['prediction'] = [str(x) for x in data['prediction']] |
| | |
| | for k in data.keys(): |
| | data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) |
| |
|
| | meta = self.data |
| | meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} |
| | data_map = {x: y for x, y in zip(data['index'], data['question'])} |
| | for k in data_map: |
| | assert k in meta_q_map, ( |
| | f'eval_file should be the same as or a subset of dataset {self.dataset_name}' |
| | ) |
| |
|
| | if circular: |
| | data = mcq_circular_eval(model, data, meta, nproc, result_file, self.dataset_name) |
| | else: |
| | data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) |
| |
|
| | |
| | dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) |
| | data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) |
| |
|
| | |
| | if 'MMT' in dataset: |
| | acc = report_acc_MMT(data) |
| | else: |
| | acc = report_acc(data) |
| |
|
| | score_file = eval_file.replace(f'.{suffix}', '_acc.csv') |
| | dump(acc, score_file) |
| |
|
| | return acc |
| |
|
| |
|
| | class CustomTextMCQDataset(TextMCQDataset): |
| |
|
| | def load_data(self, dataset): |
| | data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv') |
| |
|
| | if file_size(data_path, 'GB') > 1: |
| | local_path = data_path.replace('.tsv', '_local.tsv') |
| | if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None): |
| | from ..tools import LOCALIZE |
| | LOCALIZE(data_path, local_path) |
| | data_path = local_path |
| | return load(data_path) |
| |
|
