Upload folder using huggingface_hub

.argilla/dataset.json

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+{
+  "id": "53a2c2fe-9a3f-4245-aa35-9bcc061876d9",
+  "name": "scilake-ccam",
+  "guidelines": "# Validation guidelines for CCAM entities\n## Task Description\nYour task is to validate the extraction of the different entities and their linking to their closest matching entries in the vocabulary created for SciLake.\n\n## What to Validate\nFor each record, please verify the following:\n1. **Entity Spans:** Are all text spans correctly identified? Are the span boundaries accurate?\n2. **Entity Types:** Are entity types correctly assigned?\n3. **Entity Linking:** Are the matching entities in the vocabulary correctly assigned?\n\n## Instructions\n1. Carefully read the texts.\n2. Review the NER spans and correct them if:\n- The boundaries (start/end) are incorrect\n- The entity label is wrong\n3. Verify that the extracted entities are correctly linked to their closest match in the vocabulary\n4. Add any comments or feedback you deem relevant\n\n## Validation Guidelines\n- Entity Annotations: Mark spans as \"Correct\" only if boundaries and labels are accurate.\n- Entity Extraction: Mark as \"Correct\" if all energy (storage) types mentioned are extracted; \"Partially correct\" if any are missing or incorrect.\n- Vocabulary Linking: Mark as \"Correct\" if all links are to the appropriate entries. Use \"Partially correct\" if any are incorrect.\n\n## Entities\n- `communicationType`: the technology used for communication (eg. 4G, 5G), NOT who is connecting with whom\n- `sensorType`: the type of sensor (eg. camera, LIDAR)\n- `scenarioType`: the driving scenario (eg. cut in, lane keeping)\n- `vehicleType`: the type of vehicle (eg. car, truck)\n- `VRUType`: vulnerable road users (eg. pedestrian, cyclist)\n- `entityConnectionType`: type of connection between entities (eg. V2V, V2I), NOT the technology\n- `levelOfAutomation`: entities related to automation (eg. ALKS, driver assistance) and their relation to the FAME level of automation",
+  "allow_extra_metadata": false,
+  "status": "ready",
+  "distribution": {
+    "strategy": "overlap",
+    "min_submitted": 1
+  },
+  "metadata": null,
+  "workspace_id": "0756eadb-468f-4c06-88c4-51a3fa6f665f",
+  "last_activity_at": "2025-06-19T09:38:08.565572",
+  "inserted_at": "2025-04-09T13:03:19.498269",
+  "updated_at": "2025-04-09T13:03:20.918402"
+}

.argilla/settings.json

@@ -0,0 +1,170 @@
+{
+  "guidelines": "# Validation guidelines for CCAM entities\n## Task Description\nYour task is to validate the extraction of the different entities and their linking to their closest matching entries in the vocabulary created for SciLake.\n\n## What to Validate\nFor each record, please verify the following:\n1. **Entity Spans:** Are all text spans correctly identified? Are the span boundaries accurate?\n2. **Entity Types:** Are entity types correctly assigned?\n3. **Entity Linking:** Are the matching entities in the vocabulary correctly assigned?\n\n## Instructions\n1. Carefully read the texts.\n2. Review the NER spans and correct them if:\n- The boundaries (start/end) are incorrect\n- The entity label is wrong\n3. Verify that the extracted entities are correctly linked to their closest match in the vocabulary\n4. Add any comments or feedback you deem relevant\n\n## Validation Guidelines\n- Entity Annotations: Mark spans as \"Correct\" only if boundaries and labels are accurate.\n- Entity Extraction: Mark as \"Correct\" if all energy (storage) types mentioned are extracted; \"Partially correct\" if any are missing or incorrect.\n- Vocabulary Linking: Mark as \"Correct\" if all links are to the appropriate entries. Use \"Partially correct\" if any are incorrect.\n\n## Entities\n- `communicationType`: the technology used for communication (eg. 4G, 5G), NOT who is connecting with whom\n- `sensorType`: the type of sensor (eg. camera, LIDAR)\n- `scenarioType`: the driving scenario (eg. cut in, lane keeping)\n- `vehicleType`: the type of vehicle (eg. car, truck)\n- `VRUType`: vulnerable road users (eg. pedestrian, cyclist)\n- `entityConnectionType`: type of connection between entities (eg. V2V, V2I), NOT the technology\n- `levelOfAutomation`: entities related to automation (eg. ALKS, driver assistance) and their relation to the FAME level of automation",
+  "allow_extra_metadata": false,
+  "distribution": {
+    "strategy": "overlap",
+    "min_submitted": 1
+  },
+  "fields": [
+    {
+      "id": "269959b7-78e9-4fce-8dec-f37ef53a65a6",
+      "name": "text",
+      "title": "Text",
+      "required": true,
+      "settings": {
+        "type": "text",
+        "use_markdown": false
+      },
+      "dataset_id": "53a2c2fe-9a3f-4245-aa35-9bcc061876d9",
+      "inserted_at": "2025-04-09T13:03:19.759558",
+      "updated_at": "2025-04-09T13:03:19.759558"
+    },
+    {
+      "id": "bdc682f9-1e61-4144-90ee-8dbfe0e07cb3",
+      "name": "links",
+      "title": "Linked entities",
+      "required": true,
+      "settings": {
+        "type": "text",
+        "use_markdown": true
+      },
+      "dataset_id": "53a2c2fe-9a3f-4245-aa35-9bcc061876d9",
+      "inserted_at": "2025-04-09T13:03:19.863417",
+      "updated_at": "2025-04-09T13:03:19.863417"
+    }
+  ],
+  "questions": [
+    {
+      "id": "7c476ed4-1293-46b2-afcc-a9c9339e5e85",
+      "name": "span_label",
+      "title": "Select and classify the tokens according to the specified categories.",
+      "description": null,
+      "required": true,
+      "settings": {
+        "type": "span",
+        "field": "text",
+        "options": [
+          {
+            "value": "communicationType",
+            "text": "communicationType",
+            "description": null
+          },
+          {
+            "value": "sensorType",
+            "text": "sensorType",
+            "description": null
+          },
+          {
+            "value": "scenarioType",
+            "text": "scenarioType",
+            "description": null
+          },
+          {
+            "value": "vehicleType",
+            "text": "vehicleType",
+            "description": null
+          },
+          {
+            "value": "VRUType",
+            "text": "VRUType",
+            "description": null
+          },
+          {
+            "value": "entityConnectionType",
+            "text": "entityConnectionType",
+            "description": null
+          },
+          {
+            "value": "levelOfAutomation",
+            "text": "levelOfAutomation",
+            "description": null
+          }
+        ],
+        "visible_options": 7,
+        "allow_overlapping": true,
+        "allow_character_annotation": true
+      },
+      "dataset_id": "53a2c2fe-9a3f-4245-aa35-9bcc061876d9",
+      "inserted_at": "2025-04-09T13:03:20.046436",
+      "updated_at": "2025-04-09T13:03:20.046436"
+    },
+    {
+      "id": "94a5c85a-7b84-4137-8191-6f08c2742ab2",
+      "name": "assess_ner",
+      "title": "Extracted entity validation",
+      "description": "Are the extracted entities correct?",
+      "required": true,
+      "settings": {
+        "type": "label_selection",
+        "options": [
+          {
+            "value": "Correct",
+            "text": "Correct",
+            "description": null
+          },
+          {
+            "value": "Partially correct",
+            "text": "Partially correct",
+            "description": null
+          },
+          {
+            "value": "Incorrect",
+            "text": "Incorrect",
+            "description": null
+          }
+        ],
+        "visible_options": 3
+      },
+      "dataset_id": "53a2c2fe-9a3f-4245-aa35-9bcc061876d9",
+      "inserted_at": "2025-04-09T13:03:20.241002",
+      "updated_at": "2025-04-09T13:03:20.241002"
+    },
+    {
+      "id": "c14d6665-ab0e-4725-955e-142d0c2bb3ea",
+      "name": "assess_nel",
+      "title": "Linked vocabulary entity validation",
+      "description": "Are the linked entities in the vocabulary correct?",
+      "required": true,
+      "settings": {
+        "type": "label_selection",
+        "options": [
+          {
+            "value": "Correct",
+            "text": "Correct",
+            "description": null
+          },
+          {
+            "value": "Partially correct",
+            "text": "Partially correct",
+            "description": null
+          },
+          {
+            "value": "Incorrect",
+            "text": "Incorrect",
+            "description": null
+          }
+        ],
+        "visible_options": 3
+      },
+      "dataset_id": "53a2c2fe-9a3f-4245-aa35-9bcc061876d9",
+      "inserted_at": "2025-04-09T13:03:20.508913",
+      "updated_at": "2025-04-09T13:03:20.508913"
+    },
+    {
+      "id": "74893055-67ca-432d-8dfb-bd43fbaec3a3",
+      "name": "comments",
+      "title": "Comments",
+      "description": "Additional comments",
+      "required": false,
+      "settings": {
+        "type": "text",
+        "use_markdown": false
+      },
+      "dataset_id": "53a2c2fe-9a3f-4245-aa35-9bcc061876d9",
+      "inserted_at": "2025-04-09T13:03:20.719390",
+      "updated_at": "2025-04-09T13:03:20.719390"
+    }
+  ],
+  "metadata": [],
+  "vectors": []
+}

.argilla/version.json

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

README.md

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 ---
-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: links
-    dtype: string
-  - name: span_label.responses
-    list:
-      list:
-      - name: end
-        dtype: int64
-      - name: label
-        dtype: string
-      - name: start
-        dtype: int64
-  - name: span_label.responses.users
-    sequence: string
-  - name: span_label.responses.status
-    sequence: string
-  - name: assess_ner.responses
-    sequence: string
-  - name: assess_ner.responses.users
-    sequence: string
-  - name: assess_ner.responses.status
-    sequence: string
-  - name: assess_nel.responses
-    sequence: string
-  - name: assess_nel.responses.users
-    sequence: string
-  - name: assess_nel.responses.status
-    sequence: string
-  - name: comments.responses
-    sequence: string
-  - name: comments.responses.users
-    sequence: string
-  - name: comments.responses.status
-    sequence: string
-  - name: span_label.suggestion
-    list:
-    - name: end
-      dtype: int64
-    - name: label
-      dtype: string
-    - name: start
-      dtype: int64
-  - name: span_label.suggestion.agent
-    dtype: 'null'
-  - name: span_label.suggestion.score
-    dtype: 'null'
-  splits:
-  - name: train
-    num_bytes: 525354
-    num_examples: 191
-  download_size: 249330
-  dataset_size: 525354
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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 |
+
+
+<!-- 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
+
+# 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]