Update README.md

README.md

@@ -1,15 +1,14 @@
 ---
-license: other
+license: apache-2.0
 language:
-- zh
 - en
 size_categories:
-- n<1K
+- n>1T
 configs:
 - config_name: default
   data_files:
   - split: full
-    path: "widesearch.jsonl"
+    path: widesearch.jsonl
 ---
 # WideSearch: Benchmarking Agentic Broad Info-Seeking
 
@@ -19,7 +18,7 @@ WideSearch is a benchmark designed to evaluate the capabilities of Large Languag
 
 The challenge in these tasks lies not in cognitive difficulty, but in the operational scale, repetitiveness, and the need for **Completeness** and **Factual Fidelity** in the final result. For example, a financial analyst gathering key metrics for all companies in a sector, or a job seeker collecting every vacancy that meets their criteria.
 
-The benchmark, originating from the research paper "WideSearch: Benchmarking Agentic Broad Info-Seeking," contains 200 meticulously designed tasks (100 in English, 100 in Chinese).
+The benchmark, originating from the research paper "WideSearch: Benchmarking Agentic Broad Info-Seeking," contains 200 meticulously designed tasks (100 in English, 100 in English).
 
 See our [paper](https://arxiv.org/abs/2508.07999) and [github repo](https://github.com/ByteDance-Seed/WideSearch) for more details.
 
@@ -33,7 +32,6 @@ The dataset consists of these components: a task file, and a directory containin
 ├── widesearch.jsonl
 └── widesearch_gold/
   ├── ws_en_001.csv
-  ├── ws_zh_001.csv
   └── ...
 
 ```
@@ -71,8 +69,7 @@ The dataset consists of these components: a task file, and a directory containin
       * `unique_columns` (list): The primary key column(s) used to uniquely identify a row in the table.
       * `required` (list): All column names that must be present in the agent's generated response.
       * `eval_pipeline` (dict): Defines the evaluation method for each column.
-          * `preprocess` (list): Preprocessing steps to be applied to the cell data before evaluation (e.g., `norm_str` to normalize strings, `extract_number` to extract numbers).
-          * `metric` (list): The metric used to compare the predicted value with the ground truth (e.g., `exact_match`, `number_near` for numerical approximation, `llm_judge` for judgment by an LLM).
+          * `preprocess` (list): Preprocessing steps to be applied to the cell data before evaluation (e.g., `norm_str` to normalize strings, `extract_number`.
           * `criterion` (float or string): Specific criteria for the metric. For `number_near`, this is the allowed relative tolerance; for `llm_judge`, it's the scoring guide for the "judge" LLM.
   * `language` (string): The language of the task (`en` or `zh`).