Use three-digit CPT shard filenames

README.md

@@ -29,10 +29,74 @@ TransitLM is a dataset for public transit route planning in Chinese urban enviro
 | `transit-lm-general-benchmark*-test.csv` | Real-world benchmark test sets (real station names + coordinates) | §1 |
 | `transit-lm-station-info.csv` | Static station-level reference information | — |
 | `transit-lm-station-ids.json` | Station ID list covering the released transit network | — |
-| `transit-lm-cpt-corpus/transit-lm-cpt-corpus-part*.csv` | CPT corpus shards with unified sequential filenames | §2 |
+| `transit-lm-cpt-corpus/transit-lm-cpt-corpus-part*.csv` | CPT corpus shards with unified three-digit sequential filenames (e.g. `part001`) | §2 |
 
 All released data follows the anonymization principles described in our paper. The sections below explain the data treatment and evaluation protocol for each component.
 
+### File Format Examples
+
+#### 1. Anonymized SFT train/test (`transit-lm-benchmark*-train.csv`, `transit-lm-benchmark*-test.csv`)
+
+Columns:
+`task_id, req_type, adcode, indexid, system_prompt, sft_prompt, sft_label`
+
+Example:
+
+```csv
+task_id,req_type,adcode,indexid,system_prompt,sft_prompt,sft_label
+benchmark1-train,0,310110,a02385072c754b19067c70af8bb62de1,"你是公共交通路线规划专家，熟悉公共交通网络。...","{""query"":""我需要从上海北外滩亚朵酒店到上海市公安局治安总队一大队，请告诉我乘车方案"",""start"":""121.515027,31.260541"",""end"":""121.490488,31.209797"",""city"":""上海""}","{""line_sequence"":[""地铁4号线内圈(宜山路--宜山路)""],""station_sequence"":[""bus-1669599"",""bus-1670350"",""bus-1670805""],""total_distance"":""10.7公里"",""total_time"":""44分钟"",""total_fare"":""4"",""start_transfer_mode"":""步行"",""end_transfer_mode"":""步行"",""start_transfer_distance"":""523米"",""end_transfer_distance"":""1.1公里""}"
+```
+
+#### 2. Real-world benchmark (`transit-lm-general-benchmark*-test.csv`)
+
+Columns:
+`task_id, req_type, adcode, indexid, system_prompt, sft_prompt, sft_label`
+
+Example:
+
+```csv
+task_id,req_type,adcode,indexid,system_prompt,sft_prompt,sft_label
+general_benchmark1-test,0,440307,04258df410d3ec5d53e64de9f49b26be,"你是公共交通路线规划专家，熟悉公共交通网络。...","{""query"":""我在佳兆业君汇公馆，想去石芽岭(地铁站)，怎么坐车？"",""start"":""114.130533,22.692349"",""end"":""114.137989,22.628479"",""city"":""深圳""}","{""line_sequence"":[""977路(启英学校--田贝总站)""],""station_sequence"":[""平湖派出所"",""平湖中心小学"",""富伟厂""],""total_distance"":""10.3公里"",""total_time"":""46分钟"",""total_fare"":""2"",""start_transfer_mode"":""步行"",""end_transfer_mode"":""步行"",""start_transfer_distance"":""185米"",""end_transfer_distance"":""77米""}"
+```
+
+#### 3. Station reference (`transit-lm-station-info.csv`)
+
+Columns:
+`stop_id, ad_code, coord_x, coord_y, next_hop_stations`
+
+Example:
+
+```csv
+stop_id,ad_code,coord_x,coord_y,next_hop_stations
+bus-1000071,110117,117.090101,40.130034,"[""bus-1000202"", ""bus-1000089"", ""bus-1000087"", ""bus-1000071""]"
+```
+
+#### 4. Station ID list (`transit-lm-station-ids.json`)
+
+Example:
+
+```json
+[
+  "bus-1000071",
+  "bus-1000074",
+  "bus-1000082"
+]
+```
+
+#### 5. CPT corpus shard (`transit-lm-cpt-corpus/transit-lm-cpt-corpus-part*.csv`)
+
+Columns:
+`id, content, pt`
+
+The `pt` field is used only as a partition marker inherited from the original sharding pipeline. It carries no semantic meaning for modeling or evaluation.
+
+Example:
+
+```csv
+id,content,pt
+session_65703461_0,"路线规划。规划城市：北京，起点坐标：116.402471,39.933658，终点坐标：116.068893,39.648444。有以下几种方案：方案1（纯打车）... 方案2（公交+地铁）... 经过bus-900579，bus-898547，...，bus-857245",p01
+```
+
 **Overview.** We release two parallel test sets that correspond one-to-one, serving different model categories:
 
 - **Anonymized SFT test set** (30K samples). For models trained on our anonymized corpus. These models have only seen numeric station IDs and perturbed coordinates, so they must be evaluated in the same representation space.