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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
task: string
chunks: struct<POI: list<item: string>, News: list<item: string>, HopBA: list<item: string>>
child 0, POI: list<item: string>
child 0, item: string
child 1, News: list<item: string>
child 0, item: string
child 2, HopBA: list<item: string>
child 0, item: string
messages: list<item: struct<role: string, content: list<item: struct<type: string, text: string>>>>
child 0, item: struct<role: string, content: list<item: struct<type: string, text: string>>>
child 0, role: string
child 1, content: list<item: struct<type: string, text: string>>
child 0, item: struct<type: string, text: string>
child 0, type: string
child 1, text: string
answer: string
prompt: string
thinking: list<item: null>
child 0, item: null
target: string
to
{'task': Value('string'), 'chunks': {'POI': {'tower': Value('int64'), 'generator': Value('int64'), 'restaurant': Value('int64'), 'cafe': Value('int64'), 'information': Value('int64'), 'fast_food': Value('int64'), 'parking': Value('int64'), 'playground': Value('int64'), 'viewpoint': Value('int64'), 'supermarket': Value('int64'), 'clothes': Value('int64'), 'convenience': Value('int64'), 'pharmacy': Value('int64'), 'pub': Value('int64'), 'place_of_worship': Value('int64'), 'fuel': Value('int64'), 'post_office': Value('int64'), 'bakery': Value('int64'), 'bank': Value('int64'), 'fitness_centre': Value('int64'), 'bar': Value('int64'), 'beauty': Value('int64'), 'dentist': Value('int64'), 'doctors': Value('int64'), 'recycling': Value('int64'), 'clinic': Value('int64'), 'charging_station': Value('int64'), 'attraction': Value('int64'), 'butcher': Value('int64'), 'library': Value('int64'), 'hotel': Value('int64'), 'taxi': Value('int64'), 'department_store': Value('int64'), 'furniture': Value('int64'), 'jewelry': Value('int64'), 'greengrocer': Value('int64'), 'shoes': Value('int64'), 'electronics': Value('int64'), 'kindergarten': Value('int64'), 'ice_cream': Value('int64'), 'community_centre': Value('int64'), 'veterinary': Value('int64'), 'swimming_pool': Value('int64'), 'school': Value('int64'), 'gallery': Value('int64'), 'park': Value('int64'), 'optician': Value('int64'), 'car_wash': Value('int64'), 'museum': Value('int64'), 'substation': Value('int64'), 'motel': Value('int64'), 'sports_centre': Value('int64'), 'hardware': Value('int64'), 'car_rental': Value('int64'), 'garden': Value('int64'), 'police': Value('int64'), 'hostel': Value('int64'), 'fire_station': Value('int64'), 'theatre': Value('int64'), 'cinema': Value('int64'), 'food_court': Value('int64'), 'college': Value('int64'), 'marina': Value('int64'), 'bus_station': Value('int64'), 'mall': Value('int64'), 'townhall': Value('int64'), 'nightclub': Value('int64'), 'courthouse': Value('int64'), 'water_tower': Value('int64'), 'hospital': Value('int64'), 'lighthouse': Value('int64'), 'university': Value('int64'), 'casino': Value('int64'), 'golf_course': Value('int64'), 'stadium': Value('int64'), 'prison': Value('int64'), 'windmill': Value('int64'), 'public_building': Value('int64'), 'communications_tower': Value('int64')}, 'News': List(Value('string')), 'HopBA': List(Value('null'))}, 'prompt': Value('string'), 'thinking': List(Value('null')), 'answer': Value('string'), 'target': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 609, in wrapped
for item in generator(*args, **kwargs):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
task: string
chunks: struct<POI: list<item: string>, News: list<item: string>, HopBA: list<item: string>>
child 0, POI: list<item: string>
child 0, item: string
child 1, News: list<item: string>
child 0, item: string
child 2, HopBA: list<item: string>
child 0, item: string
messages: list<item: struct<role: string, content: list<item: struct<type: string, text: string>>>>
child 0, item: struct<role: string, content: list<item: struct<type: string, text: string>>>
child 0, role: string
child 1, content: list<item: struct<type: string, text: string>>
child 0, item: struct<type: string, text: string>
child 0, type: string
child 1, text: string
answer: string
prompt: string
thinking: list<item: null>
child 0, item: null
target: string
to
{'task': Value('string'), 'chunks': {'POI': {'tower': Value('int64'), 'generator': Value('int64'), 'restaurant': Value('int64'), 'cafe': Value('int64'), 'information': Value('int64'), 'fast_food': Value('int64'), 'parking': Value('int64'), 'playground': Value('int64'), 'viewpoint': Value('int64'), 'supermarket': Value('int64'), 'clothes': Value('int64'), 'convenience': Value('int64'), 'pharmacy': Value('int64'), 'pub': Value('int64'), 'place_of_worship': Value('int64'), 'fuel': Value('int64'), 'post_office': Value('int64'), 'bakery': Value('int64'), 'bank': Value('int64'), 'fitness_centre': Value('int64'), 'bar': Value('int64'), 'beauty': Value('int64'), 'dentist': Value('int64'), 'doctors': Value('int64'), 'recycling': Value('int64'), 'clinic': Value('int64'), 'charging_station': Value('int64'), 'attraction': Value('int64'), 'butcher': Value('int64'), 'library': Value('int64'), 'hotel': Value('int64'), 'taxi': Value('int64'), 'department_store': Value('int64'), 'furniture': Value('int64'), 'jewelry': Value('int64'), 'greengrocer': Value('int64'), 'shoes': Value('int64'), 'electronics': Value('int64'), 'kindergarten': Value('int64'), 'ice_cream': Value('int64'), 'community_centre': Value('int64'), 'veterinary': Value('int64'), 'swimming_pool': Value('int64'), 'school': Value('int64'), 'gallery': Value('int64'), 'park': Value('int64'), 'optician': Value('int64'), 'car_wash': Value('int64'), 'museum': Value('int64'), 'substation': Value('int64'), 'motel': Value('int64'), 'sports_centre': Value('int64'), 'hardware': Value('int64'), 'car_rental': Value('int64'), 'garden': Value('int64'), 'police': Value('int64'), 'hostel': Value('int64'), 'fire_station': Value('int64'), 'theatre': Value('int64'), 'cinema': Value('int64'), 'food_court': Value('int64'), 'college': Value('int64'), 'marina': Value('int64'), 'bus_station': Value('int64'), 'mall': Value('int64'), 'townhall': Value('int64'), 'nightclub': Value('int64'), 'courthouse': Value('int64'), 'water_tower': Value('int64'), 'hospital': Value('int64'), 'lighthouse': Value('int64'), 'university': Value('int64'), 'casino': Value('int64'), 'golf_course': Value('int64'), 'stadium': Value('int64'), 'prison': Value('int64'), 'windmill': Value('int64'), 'public_building': Value('int64'), 'communications_tower': Value('int64')}, 'News': List(Value('string')), 'HopBA': List(Value('null'))}, 'prompt': Value('string'), 'thinking': List(Value('null')), 'answer': Value('string'), 'target': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1343, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
task string | chunks dict | prompt string | thinking list | answer string | target string |
|---|---|---|---|---|---|
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8754.0, 9022.0, 9184.0, 9325.0, 9310.0, 9097.0, 8825.0, 8543.0, 8355.0, 8111.0, 7777.0, 7447.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7059.0, 6635.0, 6198.0, 6061.0, 6178.0, 6486.0, 7014.0, 7595.0, 7952.0, 8077.0, 8086.0, 8030.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7972.0, 7933.0, 8044.0, 8153.0, 8215.0, 8167.0, 8014.0, 7908.0, 7883.0, 7597.0, 7503.0, 7272.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [6980.0, 6560.0, 6120.0, 5980.0, 6117.0, 6449.0, 6963.0, 7471.0, 7806.0, 7959.0, 7931.0, 7885.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7776.0, 7860.0, 7997.0, 8144.0, 8438.0, 8602.0, 8563.0, 8331.0, 8276.0, 7968.0, 7732.0, 7477.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7181.0, 6726.0, 6270.0, 6130.0, 6253.0, 6553.0, 7078.0, 7585.0, 7950.0, 8120.0, 8272.0, 8399.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8627.0, 8737.0, 9057.0, 9320.0, 9614.0, 9647.0, 9321.0, 8912.0, 8673.0, 8306.0, 8043.0, 7738.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7293.0, 6847.0, 6306.0, 6096.0, 6107.0, 6210.0, 6545.0, 7073.0, 7639.0, 8050.0, 8384.0, 8686.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [9076.0, 9452.0, 9864.0, 10248.0, 10544.0, 10659.0, 10581.0, 10206.0, 9946.0, 9397.0, 8850.0, 8220.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7573.0, 7058.0, 6625.0, 6481.0, 6376.0, 6372.0, 6623.0, 7273.0, 8144.0, 9193.0, 10107.0, 10733.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [11124.0, 11477.0, 11691.0, 11953.0, 12091.0, 12126.0, 11584.0, 10863.0, 10371.0, 9616.0, 8982.0, 8267.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7766.0, 7184.0, 6777.0, 6743.0, 6887.0, 7312.0, 8089.0, 8943.0, 9556.0, 9929.0, 10309.0, 10681.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [11026.0, 11541.0, 11836.0, 12088.0, 11999.0, 11448.0, 10970.0, 10527.0, 10222.0, 9637.0, 9017.0, 8402.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7896.0, 7419.0, 7071.0, 6943.0, 6931.0, 7355.0, 8143.0, 8896.0, 9274.0, 9472.0, 9470.0, 9498.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [9517.0, 9826.0, 9987.0, 10323.0, 10585.0, 10800.0, 10341.0, 9592.0, 9068.0, 8523.0, 8069.0, 7723.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7360.0, 6907.0, 6512.0, 6401.0, 6508.0, 6898.0, 7574.0, 8181.0, 8488.0, 8500.0, 8545.0, 8474.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8456.0, 8505.0, 8601.0, 8791.0, 8897.0, 8876.0, 8646.0, 8484.0, 8382.0, 8060.0, 7791.0, 7535.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7185.0, 6712.0, 6311.0, 6249.0, 6435.0, 6887.0, 7537.0, 8145.0, 8541.0, 8657.0, 8754.0, 8819.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8828.0, 8895.0, 8981.0, 9068.0, 9322.0, 9387.0, 9287.0, 9061.0, 8873.0, 8452.0, 8296.0, 7999.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7594.0, 7146.0, 6772.0, 6583.0, 6709.0, 7164.0, 7888.0, 8573.0, 9008.0, 9224.0, 9428.0, 9431.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [9740.0, 10122.0, 10606.0, 11006.0, 11286.0, 11200.0, 10835.0, 10363.0, 10121.0, 9642.0, 9173.0, 8702.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8120.0, 7480.0, 6964.0, 6729.0, 6704.0, 6799.0, 7112.0, 7693.0, 8273.0, 8627.0, 8979.0, 9254.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [9500.0, 9554.0, 9494.0, 9495.0, 9417.0, 9185.0, 8914.0, 8500.0, 8392.0, 8081.0, 7668.0, 7341.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [6983.0, 6598.0, 6144.0, 6046.0, 6035.0, 6076.0, 6188.0, 6469.0, 6692.0, 6776.0, 6734.0, 6684.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [6596.0, 6612.0, 6664.0, 6834.0, 7102.0, 7326.0, 7468.0, 7450.0, 7550.0, 7261.0, 7128.0, 6864.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [6622.0, 6283.0, 5995.0, 5919.0, 6134.0, 6622.0, 7289.0, 7728.0, 7873.0, 7895.0, 7875.0, 7847.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7883.0, 7910.0, 7915.0, 8113.0, 8354.0, 8475.0, 8477.0, 8290.0, 8257.0, 7958.0, 7688.0, 7479.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7162.0, 6754.0, 6304.0, 6155.0, 6339.0, 6871.0, 7503.0, 7863.0, 8065.0, 8090.0, 8075.0, 7990.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7835.0, 7823.0, 7804.0, 7905.0, 8204.0, 8342.0, 8288.0, 8080.0, 8042.0, 7698.0, 7534.0, 7343.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7058.0, 6653.0, 6194.0, 6036.0, 6154.0, 6628.0, 7288.0, 7710.0, 7884.0, 7886.0, 7836.0, 7806.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7855.0, 7903.0, 8051.0, 8256.0, 8510.0, 8691.0, 8615.0, 8329.0, 8186.0, 7836.0, 7555.0, 7314.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7010.0, 6559.0, 6162.0, 6071.0, 6209.0, 6664.0, 7328.0, 7759.0, 7996.0, 8111.0, 8159.0, 8277.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8390.0, 8660.0, 9006.0, 9422.0, 9749.0, 9918.0, 9714.0, 9252.0, 9007.0, 8454.0, 8062.0, 7683.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7312.0, 6919.0, 6446.0, 6320.0, 6481.0, 6927.0, 7605.0, 8111.0, 8409.0, 8600.0, 8898.0, 9237.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [9655.0, 10149.0, 10510.0, 10709.0, 10898.0, 10689.0, 10272.0, 9603.0, 9203.0, 8617.0, 8229.0, 7831.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7364.0, 6845.0, 6393.0, 6187.0, 6207.0, 6372.0, 6687.0, 7128.0, 7632.0, 7882.0, 8116.0, 8444.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8786.0, 9210.0, 9585.0, 9883.0, 9943.0, 9924.0, 9735.0, 9280.0, 9045.0, 8578.0, 8184.0, 7643.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7224.0, 6824.0, 6440.0, 6262.0, 6228.0, 6303.0, 6481.0, 6903.0, 7371.0, 7795.0, 8132.0, 8502.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [8928.0, 9260.0, 9679.0, 10030.0, 10321.0, 10475.0, 10338.0, 9859.0, 9518.0, 8875.0, 8300.0, 7754.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7255.0, 6838.0, 6531.0, 6424.0, 6593.0, 7091.0, 7953.0, 8654.0, 9136.0, 9575.0, 10101.0, 10589.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [11158.0, 11656.0, 12020.0, 12366.0, 12449.0, 12008.0, 11402.0, 10768.0, 10324.0, 9581.0, 8927.0, 8305.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [7844.0, 7364.0, 6955.0, 6868.0, 7019.0, 7553.0, 8357.0, 9083.0, 9528.0, 9770.0, 9840.0, 9944.0]} | |
prediction | {
"POI": {
"tower": 38020,
"generator": 23149,
"restaurant": 15660,
"cafe": 13429,
"information": 13083,
"fast_food": 7902,
"parking": 5378,
"playground": 5095,
"viewpoint": 4824,
"supermarket": 3954,
"clothes": 3587,
"convenience": 2786,
"pharmacy": 2636,
"pub"... | <s>[INST]<<SYS>>
Role: You are a professional electricity load forecasting expert, using spatial and historical data to forecast future system load.
Use the 12 past flow data plus multimodality attributes to predict the next 12 flow values.
<</SYS>>
</s>
- Australian Energy Market Operator (AEMO)
Given the contexts: [c... | [] | {"Next 12 flow values": [10071.0, 10153.0, 10274.0, 10449.0, 10634.0, 10576.0, 10443.0, 10192.0, 10003.0, 9305.0, 8734.0, 8278.0]} |
End of preview.
TransCity-VLM Dataset
The TransCity-VLM Dataset provides multimodal smart-city data for traffic, energy, mobility, grid operation, urban context understanding, and map-grounded question answering. It supports the training and evaluation of vision-language models for urban prediction, decision support, conversational QA, and reasoning tasks.
The training data are available at this Hugging Face dataset repository.
Dataset Summary
| Split | Rows / Files |
|---|---|
| test JSONL rows | 308,642 |
| large-scale QA rows | 101,736 |
| multi-turn QA rows | 138,558 |
| COT QA rows | 37,713 |
| map screenshot files | 28,055 |
Dataset Components
| Component | Content | Typical use |
|---|---|---|
test/ |
Forecasting-style JSONL records for traffic and energy domains across multiple regions and time windows. | Checkpoint evaluation and reproducibility. |
qa_large_scale/ |
Single-turn QA records covering traffic, energy, bus mobility, grid operation, site context, relevance, and explanation tasks. | Instruction tuning and QA training. |
qa_multi_turn/ |
Multi-turn smart-city dialogues spanning traffic, energy, bus, grid, and urban context tasks. | Dialogue modeling and conversational QA training. |
qa_cot/ |
QA records with reasoning traces for prediction, explanation, relevance, bus, grid, traffic, and energy tasks. | Reasoning-oriented training and evaluation. |
map_screenshots/ |
Map screenshot images for Beijing, GLA, Glasgow, Grid, Nezaland, and Singapore. | Visual grounding and map-based VQA. |
preview/ |
Lightweight examples from the forecasting, QA, reasoning, dialogue, and GLA image components. | Quick inspection in Dataset Preview. |
Coverage
| Aspect | Description |
|---|---|
| Regions and cities | Beijing, GLA, Glasgow, Singapore, Nezaland, Grid, and regional traffic or electricity collections. |
| Time windows | 1h, 3h, 12h, 24h, and 72h windows, depending on the task and domain. |
| Domains | Traffic flow, electricity and energy, bus operations, grid operation, urban site context, and map-grounded visual context. |
| QA tasks | Prediction, explanation, relevance assessment, decision support, multi-turn dialogue, and chain-of-thought reasoning. |
| Modalities | JSONL text and structured records, together with map screenshot images. |
Format
Each JSONL line is a single JSON object. Image files are provided in the map screenshot folders and can be used as visual context for map-grounded tasks.
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