Adding data fields for datasets

README.md

@@ -584,28 +584,106 @@ As this dataset contains very heterogenous tasks, almost every dataset as a diff
 
 ### Data Instances
 
-TO DO
+Please report to the dataset viewer to see what an instance for each dataset looks like.
 
 ### Data Fields
 
-TO DO
+We will give for each dataset the data fields.
+
+- **GeoQuestions1089_coord**:
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`List[float]`) : the coordinates of the answer. The first element of the list correspond to the latitude and the second to the longitude.
+- **GeoQuestions1089_YN**:
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`List[bool]`) : a list containing the boolean corresponding to the answer.
+- **GeoQuestions1089_regression** and **GeoQuery_regression**:
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`List[float]`) : a list containing the numbers to be predicted.
+- **GeoQuestions1089_place** and **GeoQuery_place**:
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`List[str]`) : a list containing the names of the places to be predicted.
+- **Ms-Marco_place**:
+    - `question_id`(`int64`) : the id of the question from the original dataset.
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`str`) : the answer to the question formulated by a human.
+    - `passages`(`List[dict]`) : a list of dicts. Each dict correspond to a passage and gives the following information:
+      - `is_selected`(`int64`) : 1 if the passage was selected to write the answer, 0 otherwise.
+      - `passage_text`(`str`) : the text of the passage.
+      - `url`(`str`) : the url from where the passage was retrieved.
+- **GeoSQA**:
+    - `question_id`(`int64`) : the id of the question from the original dataset.
+    - `scenario_id`(`int64`) : the id of the scenario from the original dataset.
+    - `annotation`(`str`) : the description of the image normally used to answer the question.
+    - `scenario`(`str`) : the scenario attached to the image providing context to the question.
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`str`) : the letter corresponding to the right choice.
+    - `A`(`str`) : one of the possibles answers to the question.
+    - `B`(`str`) : one of the possibles answers to the question.
+    - `C`(`str`) : one of the possibles answers to the question.
+    - `D`(`str`) : one of the possibles answers to the question.
+- **GKMC**:
+    - `question_id`(`int64`) : the id of the question from the original dataset.
+    - `scenario`(`str`) : the scenario providing context to the question.
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`str`) : the letter corresponding to the right choice.
+    - `A`(`str`) : one of the possibles answers to the question.
+    - `B`(`str`) : one of the possibles answers to the question.
+    - `C`(`str`) : one of the possibles answers to the question.
+    - `D`(`str`) : one of the possibles answers to the question.
+- **SpatialEvalLLM**:
+    - `scenario`(`str`) : the scenario providing context to the question.
+    - `question`(`str`) : the question to be answered.
+    - `answer`(`List[str]`) : a list containing the names of the right objects to predict.
+    - `struct_type`(`str`) : the geometric structure of the map.
+    - `size`(`str`) : the size of the structure in number of tiles composing it.
+    - `k_hop`(`str`) : the minimum reasoning steps required to answer the question.
+    - `seed`(`str`) : the seed used to generate the question.
+    - `description_level`(`str`) : if **global** then the entierity of the map is described. If **local**, only a portion of the map is described.
+- **SpartUN**:
+    - `question_id`(`str`) : the id of the question from the original dataset.
+    - `scenario_id`(`str`) : the id of the scenario from the original dataset.
+    - `scenario`(`str`) : the scenario providing context to the question.
+    - `question`(`str`) : the question to be answered.
+    - `candidates_answers`(`List[str]`) : the candidates answers from which the model has to retrieve.
+    - `answer`(`List[str]`) : a list containing the right answers from the candidate list.
+    - `type`(`str`) : **YN** from boolean questions, **FR** for Find Relation questions.
+    - `k_hop`(`int64`) : the minimum reasoning steps required to answer the question.
+- **StepGame**:
+    - `scenario`(`str`) : the scenario providing context to the question.
+    - `question`(`str`) : the question to be answered.
+    - `candidates_answers`(`List[str]`) : the candidates answers from which the model has to retrieve.
+    - `answer`(`List[str]`) : a list containing the right answers from the candidate list.
+    - `k_hop`(`int64`) : the minimum reasoning steps required to answer the question.
+- **TourismQA**:
+    - `question`(`str`) : the question to be answered.
+    - `answers_names`(`List[str]`) : a list containing the names of the POI to be recommended (answer expected).
+    - `city`(`dict`) : a dict containing the following informations about the city where take place the question:
+      - `coord`(`List[float]`) : the coordinates of the city. The first element of the list correspond to the latitude and the second to the longitude.
+      - `name`(`str`) : the name of the city.
+    - `tagged_locations`(`List[str]`) : the locations names retrieved from the question (not used for our description of the task).
+    - `tagged_locations_lat_long`(`List[flaot]`) : the latitudes and longitudes of the locations retrieved from the question (not used for our description of the task).
+    - `answers_adresses`(`List[str]`) : the postal adresses of each answer (not used for our description of the task).
+    - `answers_reviews`(`List[List[str]]`) : for each POI, we have a list of reviews (not used for our description of the task).
+    - `answers_sum_reviews`(`List[str]`) : a summarization of the reviews for each POI retrieved from ??? work (not used for our description of the task).
+    - `answers_lat_longs`(`List[str]`) : the latitudes and longitudes of the answers (not used for our description of the task).
+
 
 ### Data Splits
 
 | Category        | Tasks                  | Datasets                                 | Train                 | Dev                 | Test                      |
 | --------------- | ---------------------- | ---------------------------------------- | --------------------- | ------------------- | ------------------------- |
-| **Knowledge**   | Coordinates Prediction | GeoQuestions1089                         | –                     | –                   | 84                        |
-|                 | Yes/No questions       | GeoQuestions1089                         | –                     | –                   | 181                       |
-|                 | Regression             | GeoQuestions1089<br>GeoQuery             | –<br>180              | –<br>17             | 234<br>88                 |
-|                 | Place prediction       | GeoQuestions1089<br>GeoQuery<br>MS-Marco | –<br>348<br>23 513    | –<br>32<br>4 149    | 455<br>184<br>2 907       |
+| **Knowledge**   | Coordinates Prediction | GeoQuestions1089_coord                        | –                     | –                   | 87                        |
+|                 | Yes/No questions       | GeoQuestions1089_YN                         | –                     | –                   | 181                       |
+|                 | Regression             | GeoQuestions1089_regression<br>GeoQuery_regression             | –<br>182              | –<br>17             | 231<br>89                 |
+|                 | Place prediction       | GeoQuestions1089_place<br>GeoQuery_place<br>MS-Marco_place | –<br>346<br>23 513    | –<br>33<br>4 149    | 455<br>184<br>2 907       |
 | **──────────**  | **──────────**         | **──────────**                           | **──────────**        | **──────────**      | **──────────**            |
-| **Reasoning**   | Scenario Complex QA    | GeoSQA<br>GKMC                           | –<br>–                | –<br>–              | 4 110<br>1 600            |
+| **Reasoning**   | Scenario Complex QA    | GeoSQA<br>GKMC                           | 2 644<br>–                | 628<br>–              | 838<br>1 600            |
 |                 | Spatial Reasoning      | SpatialEvalLLM<br>SpartUN<br>StepGame    | –<br>37 095<br>50 000 | –<br>5 600<br>5 000 | 1 400<br>5 551<br>100 000 |
 | **──────────**  | **──────────**         | **──────────**                           | **──────────**        | **──────────**      | **──────────**            |
-| **Application** | POI Recommendation     | TourismQA<br>NY-QA                       | 19 960<br>–           | 2 119<br>–          | 2 173<br>1 347            |
-|                 | Path Finding           | bAbI (task 19)<br>GridRoute<br>PPNL      | 9 000<br>–<br>69 472  | 1 000<br>–<br>8 684 | 1 000<br>300<br>74 484    |
+| **Application** | POI Recommendation     | TourismQA<br>NY-QA                       | 19 762<br>–           | 2 109<br>–          | 2 153<br>1 347            |
+|                 | Path Finding           | GridRoute<br>PPNL_single<br>PPNL_multi  | –<br>16 032<br>53 440  | –<br>2 004<br>6 680 | 300<br>19 044<br>55 440    |
 | **──────────**  | **──────────**         | **──────────**                           | **──────────**        | **──────────**      | **──────────**            |
-| **Total**       | –                      | –                                        | **236 290**           | **29 942**          | **176 628**               |
+| **Total**       | –                      | –                                        | **203 014**           | **26 220**          | **191 807**               |