README.md

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

{"id":"ts_anomaly_0","image":"images/ts_anomaly_0.png","answer_type":"exact","conversations":["...question...","No"]}

{"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

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:

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.