Update readme

README.md

@@ -33,77 +33,15 @@ We currently evaluated 10 models on CMMU. The results are shown in the following
 | Qwen-VL-Plus               | 26.77    | 26.9      |
 | GPT-4V                     | 30.19    | 30.91     |
 
-## How to use
 
-### Load dataset
-```python
-from eval.cmmu_dataset import CmmuDataset
-# CmmuDataset will load *.jsonl files in data_root
-dataset = CmmuDataset(data_root=your_path_to_cmmu_dataset)
+## Citation
+**BibTeX:**
+```bibtex
+@article{he2024cmmu,
+        title={CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning},
+        author={Zheqi He, Xinya Wu, Pengfei Zhou, Richeng Xuan, Guang Liu, Xi Yang, Qiannan Zhu and Hua Huang},
+        journal={arXiv preprint},
+        year={2024},
+      }
 ```
 
-**About fill-in-the-blank questions**
-
-For fill-in-the-blank questions, `CmmuDataset` will generate new questions by `sub_question`, for example:
-
-The original question is:
-```python
-{
-    "type": "fill-in-the-blank",
-    "question_info": "question", 
-    "id": "subject_1234", 
-    "sub_questions": ["sub_question_0", "sub_question_1"],
-    "answer": ["answer_0", "answer_1"]
-}
-```
-Converted questions are:
-```python
-[
-{
-    "type": "fill-in-the-blank",
-    "question_info": "question" + "sub_question_0", 
-    "id": "subject_1234-0",
-    "answer": "answer_0"
-},
-{
-    "type": "fill-in-the-blank",
-    "question_info": "question" + "sub_question_1", 
-    "id": "subject_1234-1",
-    "answer": "answer_1"
-}
-]
-```
-
-**About ShiftCheck**
-The parameter `shift_check` is `True` by default, you can get more information about `shift_check` in our technical report.
-
-`CmmuDataset` will generate k new questions by `shift_check`, their ids are `{original_id}-k`.
-
-
-## Evaluate
-
-The output format should be a list of json dictionaries, the required key is as follows:
-```python
-{
-    "question_id": "question id",
-    "answer": "answer"
-}
-``` 
-Current code call gpt4 API by `AzureOpenAI`, maybe you need to modify `eval/chat_llm.py` to create your own client, and before run evaluation, you need to set environment variables like `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT`.
-
-Run 
-```shell
-python eval/evaluate.py --result your_pred_file --data_root your_path_to_cmmu_dataset
-```
-
-**NOTE** We evaluate fill-in-the-blank questions using GPT-4 by default. If you do not have access to GPT-4, you can attempt to use a rule-based method to fill in the blanks. However, be aware that the results might differ from the official ones.
-```shell
-python eval/evaluate.py --result your_pred_file --data_root your_path_to_cmmu_dataset --gpt none
-```
-
-To evaluate specific type of questions, you can use `--qtype` parameter, for example:
-```shell
-python eval/evaluate.py --result example/gpt4v_results_val.json --data_root your_path_to_cmmu_dataset --qtype fbq mrq
-```
-
-## Citation

hf_cmmu_dataset.py

@@ -0,0 +1,46 @@
+from datasets import DatasetBuilder, Features, Value, Sequence, DatasetInfo
+import json
+import os.path as osp
+
+class HfCmmuDataset(DatasetBuilder):
+    def _info(self):
+        return DatasetInfo(
+            features=Features({
+                "type": Value("string"),
+                "class": Value("string"),
+                "grade_band": Value("string"),
+                "difficulty": Value("string"),
+                "answer": Value("string"),
+                "question_info": Value("string"),
+                "solution_info": Value("string"),
+                "options": Sequence(Value("string")),
+                "sub_questions": Sequence(Value("string")),
+                "id": Value("string"),
+                "images": Sequence(Value("string")),
+                "split": Value("string")
+            }),
+        )
+
+    def _split_generators(self, dl_manager):
+        # Define the splits here if needed
+        return []
+
+    def _generate_examples(self):
+        # Here, you'll need to write code to read and yield each example
+        # from your jsonl files.
+        jsonl_files = [
+            "biology.jsonl",
+            "chemistry.jsonl",
+            "geography.jsonl",
+            "history.jsonl",
+            "math.jsonl",
+            "physics.jsonl",
+            "politics.jsonl"
+        ]
+        for file_path in jsonl_files:  # Add all your jsonl files here
+            with open(osp.join('val', file_path), "r", encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    record = json.loads(line)
+                    yield idx, record
+
+dataset = HfCmmuDataset()