Upload folder using huggingface_hub

README.md

@@ -25,6 +25,10 @@ configs:
   data_files:
   - split: train
     path: code_only/train-*
+- config_name: image_only
+  data_files:
+  - split: train
+    path: image_only/train-*
 - config_name: d3_code_only
   data_files:
   - split: train
@@ -84,6 +88,31 @@ dataset_info:
   splits:
   - name: train
     num_examples: 3600
+- config_name: image_only
+  features:
+  - name: id
+    dtype: string
+  - name: category
+    dtype: string
+  - name: sample_id
+    dtype: string
+  - name: level
+    dtype: string
+  - name: input_type
+    dtype: string
+  - name: instruction
+    dtype: string
+  - name: GT w/o anno code
+    dtype: string
+  - name: gt_wo_anno_chart
+    dtype: image
+  - name: GT code
+    dtype: string
+  - name: gt_chart
+    dtype: image
+  splits:
+  - name: train
+    num_examples: 3600
 - config_name: d3_code_only
   features:
   - name: id
@@ -155,19 +184,19 @@ dataset_info:
 <h3 align="center">The official dataset repository of ChartAnno</h3>
 
 <p align="center">
-  <b>1,200</b> real-world charts &nbsp;·&nbsp; <b>3,600</b> instructions &nbsp;·&nbsp; <b>7,200</b> model input instances &nbsp;·&nbsp; <b>17</b> chart types
+  <b>1,200</b> real-world charts &nbsp;·&nbsp; <b>3,600</b> instructions &nbsp;·&nbsp; <b>7,200</b> primary instances (+3,600 image-only · +720 D3/SVG) &nbsp;·&nbsp; <b>3</b> representations &nbsp;·&nbsp; <b>17</b> chart types
 </p>
 
 ## 1. Data Overview
 
 Chart annotation is a communicative task that requires inferring intended messages, interpreting chart semantics, and placing appropriate textual or graphical elements. **ChartAnno** is a benchmark for evaluating multimodal large language models (MLLMs) on **chart annotation generation**: given an existing chart (as executable code, optionally with the rendered image) and an annotation instruction, the model generates executable code that adds the requested annotations.
 
-ChartAnno includes **1,200 real-world charts**, each with a paired **annotated / unannotated ground-truth** (code + rendered image) pair. Every chart is paired with annotation instructions across three levels of specificity — **Intent**, **Operation**, and **Implementation** — yielding **3,600 instruction instances**, tested under **two input settings** (`Input: code` and `Input: code+Image`) for **7,200 model input instances** in total.
+ChartAnno includes **1,200 real-world charts**, each with a paired **annotated / unannotated ground-truth** (code + rendered image) pair. Every chart is paired with annotation instructions across three levels of specificity — **Intent**, **Operation**, and **Implementation** — yielding **3,600 instruction instances**. The primary benchmark tests **two input settings** (`Input: code` and `Input: code+Image`) for **7,200 model input instances**; a supplementary `Input: Image` setting adds **3,600 image-only instances**, and a 120-chart **D3.js / SVG extension** adds **720** more. Across the benchmark the same task is instantiated in **three representations** — Python, D3.js, and SVG.
 
 <p align="center">
   <img src="assets/main-chart.png" width="95%" alt="ChartAnno task design">
 </p>
-<p align="center"><sub>Task design: two input settings (Code, Code + Image) × three instruction levels (Intent, Operation, Implementation).</sub></p>
+<p align="center"><sub>Task design of ChartAnno. The benchmark considers two primary input settings, Code and Code + Image, and three instruction levels: Intent, Operation, and Implementation. The MLLM produces generated code, which is rendered into the generated chart.</sub></p>
 
 ## 2. Task Formats
 
@@ -192,6 +221,12 @@ The dataset is organized into **configs**, one per input setting:
   <td align="center">Instruction + unannotated chart code</td>
   <td align="center">Annotated code + annotated chart</td>
 </tr>
+<tr>
+  <td align="center"><code>image_only</code></td>
+  <td align="center">Input: Image</td>
+  <td align="center">Instruction + unannotated chart image</td>
+  <td align="center">Annotated code + annotated chart</td>
+</tr>
 <tr>
   <td align="center"><code>d3_code_only</code></td>
   <td align="center">Input: code (D3.js)</td>
@@ -229,11 +264,11 @@ An example pair from the `code_image` config (`Area_1`): the model receives the
 
 ### D3.js / SVG extension (code-only)
 
-Besides the main Matplotlib benchmark, the dataset ships a **D3.js and SVG extension**: **120 real-world charts** (14 chart types), each paired with instructions across the same three levels — **360 rows per backend** (`d3_code_only`, `svg_code_only`). Rows follow the same schema as `code_only`; ids carry a `_d3` / `_svg` suffix, and the code files (`.js` / `.svg`) plus full-resolution images are included in `chartanno_d3_svg.tar.gz`.
+Besides the main Python benchmark, the dataset ships a **D3.js and SVG extension**: **120 real-world charts** (14 chart types), each paired with instructions across the same three levels — **360 rows per backend** (`d3_code_only`, `svg_code_only`). Rows follow the same schema as `code_only`; ids carry a `_d3` / `_svg` suffix, and the code files (`.js` / `.svg`) plus full-resolution images are included in `chartanno_d3_svg.tar.gz`.
 
 ## 3. Data Statistics
 
-The statistics below are computed on the main Matplotlib (Python) benchmark; the D3/SVG extension follows the same construction.
+The statistics below are computed on the main Python benchmark; the D3/SVG extension follows the same construction.
 
 **Overall scale**
 
@@ -268,6 +303,16 @@ The statistics below are computed on the main Matplotlib (Python) benchmark; the
 | Unannotated | 1075.22 | 790.45 |
 | Increment | 460.46 | 385.17 |
 
+**Code token increment across representations (120-chart extension)**
+
+On the same 120 charts, the annotation increment differs substantially across representations:
+
+| Representation | Avg. code token increment | Relative to Python |
+| --- | --- | --- |
+| Python | 395.0 | 1.00× |
+| D3.js | 1374.2 | 3.48× |
+| SVG | 2612.7 | 6.61× (1.90× D3) |
+
 **Chart type distribution (17 types)**
 
 | Type | Multi | Line | Bar | Scatter | Errorpoint | Heatmap | Pie | Area | Histogram |
@@ -304,6 +349,7 @@ The file structure of the dataset is as follows:
 data/
 ├── input_code.jsonl              # Input: code task, 3,600 rows
 ├── input_code_image.jsonl        # Input: code+Image task, 3,600 rows
+├── input_image_only.jsonl        # Input: Image task, 3,600 rows
 ├── manifest.json                 # row-count summary and schema description
 ├── README.md
 └── images/

chartanno.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:13089f234709c58d1b91024d1126b4366fe960ead5c84a070cbfda79ade082f1
-size 151494493
+oid sha256:3b44b8f790eab577e9fa05cee701a60326f90c3e2008fe88fa39e79235e7e1b4
+size 153170810

image_only/train-00000-of-00004.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:feb7ad23318a46c1e18da59f39267519d7d553a25f0ad4bbd2438eba923123ef
+size 42092506

image_only/train-00001-of-00004.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5bfe0e95e511820514a9358524f61a023daafb9c5f8d1b14461be6a1fb3a3013
+size 39222945

image_only/train-00002-of-00004.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8cd7b1e4a46b4600be85234a60a36dcb00f2e50c5bafa28038af8ab7cd6379f
+size 41895576

image_only/train-00003-of-00004.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:222ad0e671e64b4ed7803f7a4e0541cd7e50959f7ec89c1474efb32e39e77089
+size 41059928