Polish dataset card and add bilingual evaluation cover

Add the shared cover, Chinese overview, Hub quick start, file guide and tutorial links. Keep evaluation data, scoring rules, license and explicit JSONL split unchanged.

README.md

@@ -31,6 +31,22 @@ configs:
 
 # Bilingual Patch Contracts
 
+![Bilingual Patch Contracts: English, Chinese and mixed-language inputs with strict JSON evaluation](cover.png)
+
+Strict JSON state-update diagnostics in English, Chinese and mixed Chinese-English.
+
+[Run the scoring tutorial on Kaggle](https://www.kaggle.com/code/xover2022/bilingual-patch-contracts-strict-scorer) · [Kaggle dataset](https://www.kaggle.com/datasets/xover2022/bilingual-patch-contracts-dataset) · [Original benchmark and model runs](https://www.kaggle.com/benchmarks/xover2022/bilingual-patch-contracts)
+
+## 中文导读
+
+这个小型诊断集检查模型能否按顺序修改 JSON 状态，并准确返回规定的字段、类型和值。十二个语义场景各含英文、中文和中英混合版本，共 36 条提示，覆盖纠正、否定、空值、列表、单位换算、条件和字面量处理。
+
+使用 `prompt` 作为完整模型输入，以 `expected_state` 为标准答案。CSV 中的状态列需用 `json.loads` 解析；JSONL 中则直接是对象。按 `pair_id` 比较同一场景的三个语言版本。自然语言规则和指令随语言变化，JSON 字段名与路由枚举统一使用英文。
+
+答案全部公开，三个语言版本相互关联。它适合定位结构化输出问题；小样本结果不能推导通用模型排名。数据集没有新增模型成绩，Notebook 中的合成示例用于验证评分程序。
+
+## Overview
+
 A small public diagnostic set for one question: when a model is told to apply a few edits to a JSON state and return **only** the new JSON object, does it get every value right, and does that change when the instructions are written in English, Chinese, or code-switched Chinese-English?
 
 Valid JSON is not enough. The cases probe corrections ("set to 90, then correct it to zero"), negation, `null` versus `"null"` versus `""`, tag order and case, unit conversion to integer minutes, conditionals evaluated mid-sequence, instruction-like text that must be stored literally, and Unicode/backslash escaping.
@@ -75,13 +91,34 @@ Every state has the keys `route` (`"email"`, `"sms"` or `"none"`), `delay_minute
 
 ## Files
 
-- `patch_contracts.jsonl` (UTF-8, one object per line), the canonical file
-- `patch_contracts.csv` (UTF-8, all cells quoted, states as JSON text)
-- `manifest.json`: version, source file SHA-256, counts, field descriptions and data-file hashes
-- `strict_scorer.py`: the strict scorer (standard library only)
-- `LICENSE` (Apache License 2.0 full text) and `NOTICE` (copyright and provenance)
+- [`patch_contracts.jsonl`](patch_contracts.jsonl): canonical UTF-8 dataset, one object per line; initial and expected states are JSON objects
+- [`patch_contracts.csv`](patch_contracts.csv): the same 36 rows as quoted UTF-8 CSV; state columns are JSON text
+- [`manifest.json`](manifest.json): version, source SHA-256, counts, field definitions and data-file hashes
+- [`strict_scorer.py`](strict_scorer.py): strict JSON parser, schema validation and exact-value scoring; Python standard library only
+- [`LICENSE`](LICENSE) and [`NOTICE`](NOTICE): Apache-2.0 terms, copyright, attribution and provenance
+- `README.md`: dataset card, usage, scoring and limitations
+- `cover.png`: cover artwork; excluded from the evaluation split by the explicit JSONL configuration
 
-## Loading
+## Quick start
+
+Load directly from the Hub:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "WideChaos/bilingual-patch-contracts",
+    split="evaluation",
+)
+assert len(ds) == 36
+print(ds[0]["prompt"])
+```
+
+For reproducible reports, pass `revision="<commit-sha>"` to pin the dataset version. Evaluate the complete `prompt` without appending `expected_state` to the input. Keep failed calls in the denominator and report paired results across the three languages.
+
+The [Kaggle notebook](https://www.kaggle.com/code/xover2022/bilingual-patch-contracts-strict-scorer) demonstrates the strict scorer and a response-file adapter. It runs offline and uses labeled synthetic fixtures; those fixture scores are not model measurements.
+
+### Local files
 
 Standard library only:
 
@@ -122,6 +159,10 @@ All prompts, states and answers are original synthetic material that I wrote for
 - One fixed schema with five keys; it does not cover tool calling, nested objects, long contexts, latency or general multilingual ability.
 - Answers are public and may appear in future training data, which would make scores on this set uninformative.
 
+## Maintenance and feedback
+
+This is dataset version 1.0.0, a fixed diagnostic release. Documentation and presentation updates do not change the prompts or answers. For a suspected error, [open a discussion](https://huggingface.co/datasets/WideChaos/bilingual-patch-contracts/discussions) with the prompt `id`, expected behavior and a minimal reproduction. Remove private prompts, credentials and user data before posting.
+
 ## License and attribution
 
 Everything in this release, data and code alike, is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) (Apache-2.0), full text in `LICENSE`. This keeps the license under which the public Kaggle benchmark was already released; this release does not relicense it. Copyright 2026 Renchao Wu; keep `NOTICE` when redistributing.