---
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for scilake-ccam







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/scilake-ccam", 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/scilake-ccam")
```

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 | True |
| links | Linked entities | 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| span_label | Select and classify the tokens according to the specified categories. | span | True | N/A | ['communicationType', 'sensorType', 'scenarioType', 'vehicleType', 'VRUType', 'entityConnectionType', 'levelOfAutomation'] |
| assess_ner | Extracted entity validation | label_selection | True | Are the extracted entities correct? | ['Correct', 'Partially correct', 'Incorrect'] |
| assess_nel | Linked vocabulary entity validation | label_selection | True | Are the linked entities in the vocabulary correct? | ['Correct', 'Partially correct', 'Incorrect'] |
| comments | Comments | text | False | Additional comments | 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

# Validation guidelines for CCAM entities
## Task Description
Your task is to validate the extraction of the different entities and their linking to their closest matching entries in the vocabulary created for SciLake.

## What to Validate
For each record, please verify the following:
1. **Entity Spans:** Are all text spans correctly identified? Are the span boundaries accurate?
2. **Entity Types:** Are entity types correctly assigned?
3. **Entity Linking:** Are the matching entities in the vocabulary correctly assigned?

## Instructions
1. Carefully read the texts.
2. Review the NER spans and correct them if:
- The boundaries (start/end) are incorrect
- The entity label is wrong
3. Verify that the extracted entities are correctly linked to their closest match in the vocabulary
4. Add any comments or feedback you deem relevant

## Validation Guidelines
- Entity Annotations: Mark spans as "Correct" only if boundaries and labels are accurate.
- Entity Extraction: Mark as "Correct" if all energy (storage) types mentioned are extracted; "Partially correct" if any are missing or incorrect.
- Vocabulary Linking: Mark as "Correct" if all links are to the appropriate entries. Use "Partially correct" if any are incorrect.

## Entities
- `communicationType`: the technology used for communication (eg. 4G, 5G), NOT who is connecting with whom
- `sensorType`: the type of sensor (eg. camera, LIDAR)
- `scenarioType`: the driving scenario (eg. cut in, lane keeping)
- `vehicleType`: the type of vehicle (eg. car, truck)
- `VRUType`: vulnerable road users (eg. pedestrian, cyclist)
- `entityConnectionType`: type of connection between entities (eg. V2V, V2I), NOT the technology
- `levelOfAutomation`: entities related to automation (eg. ALKS, driver assistance) and their relation to the FAME level of automation

#### 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]