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.argilla/dataset.json

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+{
+  "id": "25a3e230-cc98-4072-8c34-38e2870f18bf",
+  "name": "geotagging_reranker",
+  "guidelines": "# OSM Entity Reranking Annotation Guidelines\n\n## 1  Task Description\nYou will be shown:\n* A **geographic mention** extracted from free text — e.g. “Alexanderplatz”, “Strait of Messina”.\n* A short **context snippet** (± 1–2 sentences) providing local clues.\n* A **candidate list** of OpenStreetMap (OSM) objects that our retrieval pipeline thinks might match, each with:\n  - OSM ID, object type (node / way / relation)\n  - Primary name and known alternate names\n  - Key location tags (place, amenity, natural, boundary, etc.)\n  - Lat/long, containing admin areas, and distance to any coordinates mentioned in the text (if available)\n  - System-generated similarity score (descending order)\n\nYour job is to **verify and (re)rank** these candidates so that the true match is at rank 1 or, if missing, to supply the correct OSM ID.\n\n---\n\n## 2  What to Deliver\nFor every record you must:\n\n| Field                     | What to enter                                                                             |\n|---------------------------|-------------------------------------------------------------------------------------------|\n| `top_candidate_score`     | A quality score **1–5** for the best candidate (1 = wrong object, 5 = perfect match).     |\n| `correct_osm_id_if_none`  | If no candidate is correct, paste the OSM ID (node/way/relation) you found; else leave blank. |\n| `feedback`                | Free-text comments, ambiguous cases, or anything helpful for model improvement.           |\n\n---\n\n## 3  Detailed Instructions\n1. **Read the mention & context**  \n   - Note nearby place names, feature type (city, mountain, river, square, etc.), and any coordinate clues.\n\n2. **Open each candidate** (the tool links to the OSM web viewer):  \n   - Confirm the feature’s geometry, tags, and admin location.  \n   - Check alternate names (`name:*`, `alt_name`, `official_name`) and language variants.\n\n3. **Decide correctness & rerank**  \n   - If one candidate is an exact semantic match, place it first.  \n   - If several are plausible, order them by:\n     1. Name agreement (including abbreviations & translations)  \n     2. Correct feature type (e.g., “Lake” ≠ “Town”)  \n     3. Spatial closeness to any coordinates or larger place mentioned in context  \n     4. Popularity / prominence when all else is equal",
+  "allow_extra_metadata": true,
+  "status": "ready",
+  "distribution": {
+    "strategy": "overlap",
+    "min_submitted": 1
+  },
+  "metadata": null,
+  "workspace_id": "0617b7ed-4e77-492f-bc8f-711684fe73ef",
+  "last_activity_at": "2025-07-25T12:45:42.406729",
+  "inserted_at": "2025-06-24T11:53:45.428326",
+  "updated_at": "2025-06-24T11:53:46.446311"
+}

.argilla/settings.json

@@ -0,0 +1,102 @@
+{
+  "guidelines": "# OSM Entity Reranking Annotation Guidelines\n\n## 1  Task Description\nYou will be shown:\n* A **geographic mention** extracted from free text — e.g. “Alexanderplatz”, “Strait of Messina”.\n* A short **context snippet** (± 1–2 sentences) providing local clues.\n* A **candidate list** of OpenStreetMap (OSM) objects that our retrieval pipeline thinks might match, each with:\n  - OSM ID, object type (node / way / relation)\n  - Primary name and known alternate names\n  - Key location tags (place, amenity, natural, boundary, etc.)\n  - Lat/long, containing admin areas, and distance to any coordinates mentioned in the text (if available)\n  - System-generated similarity score (descending order)\n\nYour job is to **verify and (re)rank** these candidates so that the true match is at rank 1 or, if missing, to supply the correct OSM ID.\n\n---\n\n## 2  What to Deliver\nFor every record you must:\n\n| Field                     | What to enter                                                                             |\n|---------------------------|-------------------------------------------------------------------------------------------|\n| `top_candidate_score`     | A quality score **1–5** for the best candidate (1 = wrong object, 5 = perfect match).     |\n| `correct_osm_id_if_none`  | If no candidate is correct, paste the OSM ID (node/way/relation) you found; else leave blank. |\n| `feedback`                | Free-text comments, ambiguous cases, or anything helpful for model improvement.           |\n\n---\n\n## 3  Detailed Instructions\n1. **Read the mention & context**  \n   - Note nearby place names, feature type (city, mountain, river, square, etc.), and any coordinate clues.\n\n2. **Open each candidate** (the tool links to the OSM web viewer):  \n   - Confirm the feature’s geometry, tags, and admin location.  \n   - Check alternate names (`name:*`, `alt_name`, `official_name`) and language variants.\n\n3. **Decide correctness & rerank**  \n   - If one candidate is an exact semantic match, place it first.  \n   - If several are plausible, order them by:\n     1. Name agreement (including abbreviations & translations)  \n     2. Correct feature type (e.g., “Lake” ≠ “Town”)  \n     3. Spatial closeness to any coordinates or larger place mentioned in context  \n     4. Popularity / prominence when all else is equal",
+  "allow_extra_metadata": true,
+  "distribution": {
+    "strategy": "overlap",
+    "min_submitted": 1
+  },
+  "fields": [
+    {
+      "id": "445bebf4-b9e3-4f3c-a0da-fb352d6063bf",
+      "name": "text",
+      "title": "text",
+      "required": false,
+      "settings": {
+        "type": "text",
+        "use_markdown": true
+      },
+      "dataset_id": "25a3e230-cc98-4072-8c34-38e2870f18bf",
+      "inserted_at": "2025-06-24T11:53:45.951732",
+      "updated_at": "2025-06-24T11:53:45.951732"
+    },
+    {
+      "id": "e7d323c2-68fb-4446-8b66-0327200b838d",
+      "name": "candidates",
+      "title": "Candidate organizations",
+      "required": true,
+      "settings": {
+        "type": "text",
+        "use_markdown": true
+      },
+      "dataset_id": "25a3e230-cc98-4072-8c34-38e2870f18bf",
+      "inserted_at": "2025-06-24T11:53:46.058413",
+      "updated_at": "2025-06-24T11:53:46.058413"
+    }
+  ],
+  "questions": [
+    {
+      "id": "55536afb-6e02-41dc-8802-c3a5c01c35f5",
+      "name": "candidate_rating",
+      "title": "Candidate match (0 for no-match)",
+      "description": "Select which of the candidates match the organization mention",
+      "required": true,
+      "settings": {
+        "type": "rating",
+        "options": [
+          {
+            "value": 0
+          },
+          {
+            "value": 1
+          },
+          {
+            "value": 2
+          },
+          {
+            "value": 3
+          },
+          {
+            "value": 4
+          },
+          {
+            "value": 5
+          },
+          {
+            "value": 6
+          },
+          {
+            "value": 7
+          },
+          {
+            "value": 8
+          },
+          {
+            "value": 9
+          },
+          {
+            "value": 10
+          }
+        ]
+      },
+      "dataset_id": "25a3e230-cc98-4072-8c34-38e2870f18bf",
+      "inserted_at": "2025-06-24T11:53:46.182967",
+      "updated_at": "2025-06-24T11:53:46.182967"
+    },
+    {
+      "id": "878efe1c-ea6b-4907-982f-7348dfe31775",
+      "name": "feedback",
+      "title": "Additional feedback",
+      "description": "Any other observations about this record",
+      "required": false,
+      "settings": {
+        "type": "text",
+        "use_markdown": false
+      },
+      "dataset_id": "25a3e230-cc98-4072-8c34-38e2870f18bf",
+      "inserted_at": "2025-06-24T11:53:46.304361",
+      "updated_at": "2025-06-24T11:53:46.304361"
+    }
+  ],
+  "metadata": [],
+  "vectors": []
+}

.argilla/version.json

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+{
+  "argilla": "2.6.0"
+}

README.md

@@ -1,41 +1,188 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: status
-    dtype: string
-  - name: inserted_at
-    dtype: timestamp[us]
-  - name: updated_at
-    dtype: timestamp[us]
-  - name: _server_id
-    dtype: string
-  - name: text
-    dtype: string
-  - name: candidates
-    dtype: string
-  - name: candidate_rating.responses
-    sequence: int64
-  - name: candidate_rating.responses.users
-    sequence: string
-  - name: candidate_rating.responses.status
-    sequence: string
-  - name: feedback.responses
-    sequence: string
-  - name: feedback.responses.users
-    sequence: string
-  - name: feedback.responses.status
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 5794260
-    num_examples: 1855
-  download_size: 1146782
-  dataset_size: 5794260
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+tags:
+- rlfh
+- argilla
+- human-feedback
 ---
+
+# Dataset Card for geotagging_reranking
+
+
+
+
+
+
+
+This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
+
+
+## Using this dataset with Argilla
+
+To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
+
+```python
+import argilla as rg
+
+ds = rg.Dataset.from_hub("SIRIS-Lab/geotagging_reranking", settings="auto")
+```
+
+This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.
+
+## Using this dataset with `datasets`
+
+To load the records of this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("SIRIS-Lab/geotagging_reranking")
+```
+
+This will only load the records of the dataset, but not the Argilla settings.
+
+## Dataset Structure
+
+This dataset repo contains:
+
+* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` and can be loaded independently using the `datasets` library via `load_dataset`.
+* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
+* A dataset configuration folder conforming to the Argilla dataset format in `.argilla`.
+
+The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
+
+### Fields
+
+The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.
+
+| Field Name | Title | Type | Required |
+| ---------- | ----- | ---- | -------- |
+| text | text | text | False |
+| candidates | Candidate organizations | text | True |
+
+
+### Questions
+
+The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
+
+| Question Name | Title | Type | Required | Description | Values/Labels |
+| ------------- | ----- | ---- | -------- | ----------- | ------------- |
+| candidate_rating | Candidate match (0 for no-match) | rating | True | Select which of the candidates match the organization mention | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
+| feedback | Additional feedback | text | False | Any other observations about this record | N/A |
+
+
+<!-- check length of metadata properties -->
+
+
+
+
+### Data Splits
+
+The dataset contains a single split, which is `train`.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation guidelines
+
+# OSM Entity Reranking Annotation Guidelines
+
+## 1  Task Description
+You will be shown:
+* A **geographic mention** extracted from free text — e.g. “Alexanderplatz”, “Strait of Messina”.
+* A short **context snippet** (± 1–2 sentences) providing local clues.
+* A **candidate list** of OpenStreetMap (OSM) objects that our retrieval pipeline thinks might match, each with:
+  - OSM ID, object type (node / way / relation)
+  - Primary name and known alternate names
+  - Key location tags (place, amenity, natural, boundary, etc.)
+  - Lat/long, containing admin areas, and distance to any coordinates mentioned in the text (if available)
+  - System-generated similarity score (descending order)
+
+Your job is to **verify and (re)rank** these candidates so that the true match is at rank 1 or, if missing, to supply the correct OSM ID.
+
+---
+
+## 2  What to Deliver
+For every record you must:
+
+| Field                     | What to enter                                                                             |
+|---------------------------|-------------------------------------------------------------------------------------------|
+| `top_candidate_score`     | A quality score **1–5** for the best candidate (1 = wrong object, 5 = perfect match).     |
+| `correct_osm_id_if_none`  | If no candidate is correct, paste the OSM ID (node/way/relation) you found; else leave blank. |
+| `feedback`                | Free-text comments, ambiguous cases, or anything helpful for model improvement.           |
+
+---
+
+## 3  Detailed Instructions
+1. **Read the mention & context**  
+   - Note nearby place names, feature type (city, mountain, river, square, etc.), and any coordinate clues.
+
+2. **Open each candidate** (the tool links to the OSM web viewer):  
+   - Confirm the feature’s geometry, tags, and admin location.  
+   - Check alternate names (`name:*`, `alt_name`, `official_name`) and language variants.
+
+3. **Decide correctness & rerank**  
+   - If one candidate is an exact semantic match, place it first.  
+   - If several are plausible, order them by:
+     1. Name agreement (including abbreviations & translations)  
+     2. Correct feature type (e.g., “Lake” ≠ “Town”)  
+     3. Spatial closeness to any coordinates or larger place mentioned in context  
+     4. Popularity / prominence when all else is equal
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]