---
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 |


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### 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]