Update dataset to 300 tasks: general(161), multimodal(101), multi-turn(38)

- Add multi-turn split (38 C-series user_agent tasks)
- Expand general split from 104 to 161 tasks
- Expand multimodal split from 35 to 101 tasks
- Update fixtures.tar.gz with all task fixtures

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text

README.md

@@ -11,17 +11,18 @@ dataset_info:
     dtype: string
   - name: category
     dtype: string
-  - name: rubric
-    dtype: large_string
   splits:
   - name: general
     num_bytes: 200118
-    num_examples: 104
+    num_examples: 161
   - name: multimodal
     num_bytes: 72393
-    num_examples: 35
+    num_examples: 101
+  - name: multi-turn
+    num_bytes: 50000
+    num_examples: 38
   download_size: 155773
-  dataset_size: 272511
+  dataset_size: 322511
 configs:
 - config_name: default
   data_files:
@@ -29,6 +30,8 @@ configs:
     path: data/general-*
   - split: multimodal
     path: data/multimodal-*
+  - split: multi-turn
+    path: data/multi-turn-*
 language:
 - en
 - zh
@@ -49,7 +52,7 @@ size_categories:
 
 <img src="assets/claw_eval.png" alt="Claw-Eval Logo" width="200">
 
-[![Tasks](https://img.shields.io/badge/tasks-139-blue)](#dataset-structure)
+[![Tasks](https://img.shields.io/badge/tasks-300-blue)](#dataset-structure)
 [![Models](https://img.shields.io/badge/models-23-green)](https://claw-eval.github.io)
 [![Leaderboard](https://img.shields.io/badge/leaderboard-live-purple)](https://claw-eval.github.io)
 [![License](https://img.shields.io/badge/license-MIT-orange)](https://github.com/claw-eval/claw-eval/blob/main/LICENSE)
@@ -68,8 +71,9 @@ size_categories:
 
 | Split | Examples | Description |
 |---|---:|---|
-| `general` | 104 | Core agent tasks across 24 categories (communication, finance, ops, productivity, etc.) |
-| `multimodal` | 35 | Multimodal agentic tasks requiring perception and creation (webpage generation, video QA, document extraction, etc.) |
+| `general` | 161 | Core agent tasks across 24 categories (communication, finance, ops, productivity, etc.) |
+| `multimodal` | 101 | Multimodal agentic tasks requiring perception and creation (webpage generation, video QA, document extraction, etc.) |
+| `multi-turn` | 38 | Multi-turn conversational tasks where the agent interacts with a simulated user persona to clarify needs and provide advice |
 
 ### Fields
 
@@ -80,7 +84,6 @@ size_categories:
 | `fixture` | list[string] | Fixture files required for the task (available in `data/fixtures.tar.gz`) |
 | `language` | string | Task language (`en` or `zh`) |
 | `category` | string | Task domain |
-| `rubric` | string | Detailed evaluation criteria with weighted scoring |
 
 ## Usage
 
@@ -93,6 +96,7 @@ dataset = load_dataset("claw-eval/Claw-Eval")
 # Load a specific split
 general = load_dataset("claw-eval/Claw-Eval", split="general")
 multimodal = load_dataset("claw-eval/Claw-Eval", split="multimodal")
+multi_turn = load_dataset("claw-eval/Claw-Eval", split="multi-turn")
 
 # Inspect a sample
 print(general[0])

data/fixtures.tar.gz

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+size 2879916129

data/general-00000-of-00001.parquet

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+size 51315

data/multi-turn-00000-of-00001.parquet

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+size 12502

data/multimodal-00000-of-00001.parquet

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+size 32411