Improve dataset card: Add task category, tags, and code block formatting

README.md

@@ -1,10 +1,18 @@
 ---
+language:
+- en
 license: other
 license_name: adobe-research-license
 license_link: LICENSE
-language:
-- en
+task_categories:
+- image-to-text
+tags:
+- image-captioning
+- hallucination-detection
+- evaluation
+- multimodal
 ---
+
 # [ICML 2025] Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
 
 This dataset is associated with the evaluation in our ICML 2025 paper, [Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage](https://arxiv.org/abs/2412.15484).
@@ -15,7 +23,7 @@ This dataset is associated with the evaluation in our ICML 2025 paper, [Toward R
 * python-dotenv==1.0.1
 
 ### Dataset download
-```dataset download
+```python
 from huggingface_hub import hf_hub_download
 
 local_path = hf_hub_download(
@@ -31,12 +39,12 @@ Our evaluation uses a subset of the [DOCCI](https://google.github.io/docci/) ima
 ## Captioning
 Please generate captions for the 1,000 downloaded images for captioning evaluation.
 Summarize the generated captions into a dictionary where the key is the corresponding image file name, and save it as a .json file.
-```captions file
+```json
 {
-    "aar_test_04600.jpg": <caption_aar_test_04600>,
-    "aar_test_04601.jpg": <caption_aar_test_04601>,
+    "aar_test_04600.jpg": "<caption_aar_test_04600>",
+    "aar_test_04601.jpg": "<caption_aar_test_04601>",
     ...
-    "test_00599.json": <caption_test_00599>,
+    "test_00599.json": "<caption_test_00599>",
 }
 ```
 You may refer to the [sample captions](https://github.com/adobe-research/CapMAS/blob/master/sample_captions/llava1.6-vicuna_llama3_th1.0/captions_final.json) for guidance.
@@ -45,15 +53,15 @@ You may refer to the [sample captions](https://github.com/adobe-research/CapMAS/
 Please visit our [GitHub repository](https://github.com/adobe-research/CapMAS).
 We provide the evaluation codes for the three metrics used in our paper: **Factuality**, **Coverage**, and **CLAIR** (Chan et al., EMNLP 2023). These evaluations rely on GPT-4o, so please fill in your OpenAI API key **OR** Azure OpenAI credentials in the `conf/gpt4o` file.
 ### Factuality (ours)
-```factuality
+```bash
 python eval_factuality.py --image-dir <the image directory path> --captions-file <the caption .json file path>
 ```
 ### Coverage (ours)
-```coverage
+```bash
 python eval_coverage.py --vqa-dir data/COVERAGE_TEST_VQA --captions-file <the caption .json file path>
 ```
 ### CLAIR
-```clair
+```bash
 python eval_clair.py --captions-file <the caption .json file path>
 ```