| # Format | |
| There is no single top-level `annotations.jsonl` in this directory. Instead, the annotations are split across these 5 subdirectories: | |
| - `clean_TSQA/anomaly_detection/annotations.jsonl` | |
| - `clean_TSQA/classification/annotations.jsonl` | |
| - `clean_TSQA/open_ended_qa/annotations.jsonl` | |
| - `clean_cats-bench_hard/annotations.jsonl` | |
| - `clean_timeomni/annotations.jsonl` | |
| Each line is one JSON object (JSONL format). The core fields are described below. | |
| ## Common Fields | |
| ### `id` (string) | |
| Unique sample ID. | |
| - TSQA examples: `ts_anomaly_0`, `ts_classif_1`, `ts_openqa_3` | |
| - TimeOmni example: `1_scenario_understanding_test` | |
| - CATS hard example: `ts_retrieval_perturbed__agriculture_100_test__0` | |
| This field is generated by preprocessing scripts and is used for deduplication, tracking, and evaluation alignment. | |
| ### `image` (string) | |
| Relative path to the image file (relative to the current sub-dataset directory). | |
| - TSQA / TimeOmni: usually `images/*.png` | |
| - CATS hard: usually `plots/*.jpeg` (can also be `.jpg/.png/.webp`) | |
| During training/inference, the model should load this image before answering the question. | |
| ### `answer_type` (string) | |
| Answer type, which determines how outputs should be evaluated. | |
| - `mcq`: multiple-choice (answer is typically an option letter such as `A/B/C/D`) | |
| - `exact`: exact text match (e.g., `Yes/No`, `True/False`, numbers) | |
| - `approximation`: approximate numeric answer (supported by source scripts; rarely seen in this `hg_dataset` snapshot) | |
| Source scripts: | |
| - TSQA: `tsqa.py` | |
| - TimeOmni: `testomni.py` (always `mcq`) | |
| - CATS: `cats.py` / `cats_test.py` | |
| ### `conversations` (array[string, string]) | |
| Two-element array: | |
| - `conversations[0]`: question/prompt | |
| - `conversations[1]`: gold answer | |
| This dataset uses a simplified two-turn format, not a role-tagged format like `{from: human/gpt}`. | |
| ## Extra Field in CATS hard | |
| ### `task_type` (string, only in `clean_cats-bench_hard`) | |
| Task subtype. This field exists only in CATS hard annotations and distinguishes retrieval task variants. | |
| Common value in the current data: | |
| - `ts_retrieval_perturbed` | |
| Additional possible values supported by `cats_test.py`: | |
| - `ts_retrieval_cross_domain` | |
| - `ts_retrieval_same_domain` | |
| - `caption_retrieval_cross_domain` | |
| - `caption_retrieval_perturbed` | |
| - `caption_retrieval_same_domain` | |
| ## Minimal Examples | |
| ```json | |
| {"id":"ts_anomaly_0","image":"images/ts_anomaly_0.png","answer_type":"exact","conversations":["...question...","No"]} | |
| ``` | |
| ```json | |
| {"id":"ts_retrieval_perturbed__agriculture_100_test__0","image":"plots/agriculture_100_test.jpeg","answer_type":"mcq","task_type":"ts_retrieval_perturbed","conversations":["...question...","A"]} | |
| ``` | |
| ## Mapping to Preprocessing Scripts | |
| - TSQA subsets: `/home/xinyu/ChartModel/chart/app/data_process/rl/tsqa.py` | |
| - CATS hard: `/home/xinyu/ChartModel/chart/app/data_process/rl/cats_test.py` | |
| - TimeOmni: `/home/xinyu/ChartModel/chart/app/data_process/rl/testomni.py` | |
| These scripts define field generation logic, `answer_type` assignment, and question text cleaning rules. | |
| ## Inference Example | |
| Below is an example workflow using vLLM OpenAI-compatible server plus `bon_filter.py`. | |
| ### 1) Start vLLM server | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=4,5,6,7 python -m vllm.entrypoints.openai.api_server \ | |
| --model /path/Qwen3-VL-2B-Instruct \ | |
| --host :: \ | |
| --port 8003 \ | |
| --max-model-len 8192 \ | |
| --gpu-memory-utilization 0.85 \ | |
| --limit-mm-per-prompt '{"image": 1}' \ | |
| --data-parallel-size 4 \ | |
| --trust-remote-code \ | |
| --max-num-batched-tokens 8192 | |
| ``` | |
| ### 2) Run inference with `bon_filter.py` | |
| Example on TimeOmni subset: | |
| ```bash | |
| python chart/app/rl/bon_filter.py \ | |
| --image_dir /hg_dataset/clean_timeomni \ | |
| --input_jsonl /hg_dataset/clean_timeomni/annotations.jsonl \ | |
| --output_jsonl /hg_dataset/clean_timeomni/bon.jsonl \ | |
| --model_name /path/Qwen3-VL-2B-Instruct \ | |
| --n 1 | |
| ``` | |
| You can switch `--image_dir` and `--input_jsonl` to other subsets in this dataset, for example: | |
| - `/hg_dataset/clean_TSQA/anomaly_detection` | |
| - `/hg_dataset/clean_TSQA/classification` | |
| - `/hg_dataset/clean_TSQA/open_ended_qa` | |
| - `/hg_dataset/clean_cats-bench_hard` | |
| Note: ensure the `image` field inside each JSONL is a valid relative path under `--image_dir`. | |