Update README.md

README.md

@@ -20,7 +20,7 @@ size_categories:
   - 10K<n<100K
 ---
 
-# 🧱 TSUMM-SUITE Training Set
+# 🧱 TSUMM-Suite Training Set
 
 <p align="left">
   <a href="https://arxiv.org/abs/2602.17149">
@@ -38,11 +38,11 @@ size_categories:
     />
   </a>
 
-  <a href="https://huggingface.co/datasets/TimeOmni-VL/TSUMM_SUITE_Train">
+  <a href="https://huggingface.co/datasets/TimeOmni-VL/TSUMM-Suite_Training">
     <img
-      src="https://img.shields.io/badge/TSUMM--SUITE-Dataset-orange?logo=huggingface&logoColor=white"
+      src="https://img.shields.io/badge/TSUMM--Suite-Dataset-orange?logo=huggingface&logoColor=white"
       style="display: inline-block; vertical-align: middle;"
-      alt="TSUMM-SUITE Dataset on Hugging Face"
+      alt="TSUMM-Suite Dataset on Hugging Face"
     />
   </a>
 
@@ -56,14 +56,14 @@ size_categories:
 
   <a href="https://github.com/AntonGuan/TimeOmni-VL" target="_blank" style="margin: 2px;">
     <img
-      src="https://img.shields.io/badge/TimeOmni--VL-%20Code-536af5?logo=github&logoColor=white"
+      src="https://img.shields.io/badge/TimeOmni--VL-Code-536af5?logo=github&logoColor=white"
       style="display: inline-block; vertical-align: middle;"
       alt="TimeOmni-VL Code on GitHub"
     />
   </a>
 </p>
 
-> **This repository provides the training set of TSUMM-SUITE, a multimodal dataset for unified time series understanding and generation.** It contains forecasting and imputation tasks for TS-image generation, together with six TS-image understanding tasks derived from the same time series instances. By aligning understanding tasks with generation tasks, TSUMM-SUITE enables temporal understanding to improve time series generation.
+> **This repository provides the training set of TSUMM-Suite, a multimodal dataset for unified time series understanding and generation.** It contains forecasting and imputation tasks for TS-image generation. By aligning understanding tasks with generation tasks, TSUMM-Suite enables temporal understanding to improve time series generation.
 
 ## 🎨 Task Illustration
 
@@ -71,7 +71,7 @@ size_categories:
 <img src="https://raw.githubusercontent.com/AntonGuan/TimeOmni-VL/main/figs/task.png" width="100%"/>
 </div>
 
-<p><strong>TSUMM-SUITE covers two task families:</strong></p>
+<p><strong>TSUMM-Suite covers two task families:</strong></p>
 <div style="margin-left:18px">
 
 <p><strong>Generation tasks</strong></p>
@@ -133,40 +133,39 @@ size_categories:
   </tbody>
 </table>
 
+<p style="margin-top:13px;font-size:12px;opacity:0.85">
+* Note: This release open-sources the forecasting and imputation generation data only. We do not open-source the TS-image QA (Understanding) and TSR-Suite (Reasoning) training data.
+</p>
+
+All generation samples are stored in a single `train` split (80,000 rows total). Each TS-image has resolution **896×896**. In the paper, the main TimeOmni-VL experiments use **5k samples per generation task** for training.
+
 ### 2. Data Fields
 
 Generation samples contain fields such as:
 
 - `sample_id`: unique sample identifier
-- `task_type`: `forecasting` or `imputation`
-- `instruction`: task instruction for generation
-- `source_image`: masked TS-image input
-- `target_image`: ground-truth completed TS-image
-- `target_series`: numerical target sequence
-- `generation_cot`: temporal reasoning used as generation condition
-- `metadata`: frequency, prediction length, mask ratio, number of variables, and related information
-
-Understanding samples contain fields such as:
-
-- `question_id`: unique QA identifier
-- `image`: TS-image input
-- `question`: natural-language question
-- `cot`: reasoning chain
-- `answer`: ground-truth answer
-- `qa_type`: one of QA1–QA6
-- `level`: `layout` or `signal`
+- `task`: `forecasting` or `imputation`
+- `freq`: time series frequency (e.g., `H`, `D`, `5T`)
+- `nvars`: number of variables
+- `context_len`: context length
+- `pred_len`: prediction or imputation length
+- `periodicity`: periodicity of the series
+- `input_image`: masked TS-image input (896×896)
+- `target_image`: ground-truth completed TS-image (896×896)
+- `metadata_json`: JSON string with normalization, patch layout, mask ratio, and related metadata
 
 ## 🚀 Usage
 
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("YOUR_ORG/TSUMM-SUITE")
+dataset = load_dataset("TimeOmni-VL/TSUMM-Suite_Training")
+train = dataset["train"]
+
+forecasting = train.filter(lambda x: x["task"] == "forecasting")
+imputation = train.filter(lambda x: x["task"] == "imputation")
 
-forecasting = dataset["forecasting"]
-imputation = dataset["imputation"]
-understanding = dataset["understanding"]
-reasoning = dataset["reasoning"]
+print(f"Total: {len(train)}, Forecasting: {len(forecasting)}, Imputation: {len(imputation)}")
 ```
 
 ## 📐 Evaluation
@@ -179,7 +178,7 @@ reasoning = dataset["reasoning"]
 
 ## License
 
-TSUMM-SUITE is released under the Apache 2.0 license.
+TSUMM-Suite is released under the Apache 2.0 license.
 
 ## ✍️ Citation