Upload folder using huggingface_hub

.argilla/dataset.json

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+{"id": "265d250a-0944-4450-8e0b-ed61a62ef16c", "inserted_at": "2025-09-24T06:21:01.842532", "updated_at": "2025-09-24T06:21:04.549925", "name": "my_dataset_test_2", "status": "ready", "guidelines": "These are some guidelines.", "allow_extra_metadata": false, "distribution": {"strategy": "overlap", "min_submitted": 1}, "workspace_id": "d3906023-6021-4286-a4a2-78bc34e1dca5", "last_activity_at": "2025-09-24T06:22:44.195339"}

.argilla/settings.json

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+{"guidelines": "These are some guidelines.", "questions": [{"id": "9b06a303-8b2e-47ad-b0e4-d53b508894da", "inserted_at": "2025-09-24T06:21:03.749376", "updated_at": "2025-09-24T06:21:03.749376", "name": "query", "settings": {"type": "text", "use_markdown": false}, "title": "Please enter a search query", "description": "question description", "required": true, "dataset_id": "265d250a-0944-4450-8e0b-ed61a62ef16c", "type": "text"}, {"id": "15ca743e-b68c-4a5e-940b-0cc5e4d55223", "inserted_at": "2025-09-24T06:21:04.137408", "updated_at": "2025-09-24T06:21:04.137408", "name": "rating", "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}]}, "title": "How relevant is the query to the brand?", "description": "1 = slightly relevant, 10 = very relevant", "required": true, "dataset_id": "265d250a-0944-4450-8e0b-ed61a62ef16c", "type": "rating"}], "fields": [{"id": "295b1493-791e-4d2d-bf62-ca179c8f50ae", "inserted_at": "2025-09-24T06:21:02.977633", "updated_at": "2025-09-24T06:21:02.977633", "name": "brand_id", "settings": {"type": "text", "use_markdown": false}, "title": "Brand ID", "required": true, "description": null, "dataset_id": "265d250a-0944-4450-8e0b-ed61a62ef16c", "type": "text"}, {"id": "f5b7bb17-3708-403f-ae3c-efb27e53fc83", "inserted_at": "2025-09-24T06:21:03.350469", "updated_at": "2025-09-24T06:21:03.350469", "name": "brand_name", "settings": {"type": "text", "use_markdown": false}, "title": "Brand Name", "required": true, "description": null, "dataset_id": "265d250a-0944-4450-8e0b-ed61a62ef16c", "type": "text"}], "vectors": [], "metadata": [], "allow_extra_metadata": false, "distribution": {"strategy": "overlap", "min_submitted": 1}, "mapping": null}

README.md

@@ -1,49 +1,229 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: status
-    dtype: string
-  - name: _server_id
-    dtype: string
-  - name: brand_id
-    dtype: string
-  - name: brand_name
-    dtype: string
-  - name: query.responses
-    list: string
-  - name: query.responses.users
-    list: string
-  - name: query.responses.status
-    list: string
-  - name: rating.responses
-    list: int64
-  - name: rating.responses.users
-    list: string
-  - name: rating.responses.status
-    list: string
-  - name: query.suggestion
-    dtype: string
-  - name: query.suggestion.score
-    dtype: 'null'
-  - name: query.suggestion.agent
-    dtype: 'null'
-  - name: rating.suggestion
-    dtype: int64
-  - name: rating.suggestion.score
-    dtype: 'null'
-  - name: rating.suggestion.agent
-    dtype: 'null'
-  splits:
-  - name: train
-    num_bytes: 2722
-    num_examples: 10
-  download_size: 10351
-  dataset_size: 2722
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+size_categories: n<1K
+tags:
+- rlfh
+- argilla
+- human-feedback
 ---
+
+# Dataset Card for test
+
+
+
+
+
+
+
+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("rustem-burkhanov-careem/test", 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("rustem-burkhanov-careem/test")
+```
+
+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 | Markdown |
+| ---------- | ----- | ---- | -------- | -------- |
+| brand_id | Brand ID | text | True | False |
+| brand_name | Brand Name | text | True | False |
+
+
+### 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 |
+| ------------- | ----- | ---- | -------- | ----------- | ------------- |
+| query | Please enter a search query | text | True | question description | N/A |
+| rating | How relevant is the query to the brand? | rating | True | 1 = slightly relevant, 10 = very relevant | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
+
+
+<!-- check length of metadata properties -->
+
+
+
+
+
+### Data Instances
+
+An example of a dataset instance in Argilla looks as follows:
+
+```json
+{
+    "_server_id": "994b8e4f-c871-48f0-b412-818ef14bdf54",
+    "fields": {
+        "brand_id": "123",
+        "brand_name": "McDonald\u0027s"
+    },
+    "id": "0cd61cb8-ebda-4d84-ac88-a0354ce4928b",
+    "metadata": {},
+    "responses": {
+        "query": [
+            {
+                "user_id": "4d9329fe-4e99-4018-ac42-1122209c9502",
+                "value": "sadf"
+            }
+        ],
+        "rating": [
+            {
+                "user_id": "4d9329fe-4e99-4018-ac42-1122209c9502",
+                "value": 2
+            }
+        ]
+    },
+    "status": "completed",
+    "suggestions": {
+        "query": {
+            "agent": null,
+            "score": null,
+            "value": ""
+        },
+        "rating": {
+            "agent": null,
+            "score": null,
+            "value": 1
+        }
+    },
+    "vectors": {}
+}
+```
+
+While the same record in HuggingFace `datasets` looks as follows:
+
+```json
+{
+    "_server_id": "994b8e4f-c871-48f0-b412-818ef14bdf54",
+    "brand_id": "123",
+    "brand_name": "McDonald\u0027s",
+    "id": "0cd61cb8-ebda-4d84-ac88-a0354ce4928b",
+    "query.responses": [
+        "sadf"
+    ],
+    "query.responses.status": [
+        "submitted"
+    ],
+    "query.responses.users": [
+        "4d9329fe-4e99-4018-ac42-1122209c9502"
+    ],
+    "query.suggestion": "",
+    "query.suggestion.agent": null,
+    "query.suggestion.score": null,
+    "rating.responses": [
+        2
+    ],
+    "rating.responses.status": [
+        "submitted"
+    ],
+    "rating.responses.users": [
+        "4d9329fe-4e99-4018-ac42-1122209c9502"
+    ],
+    "rating.suggestion": 1,
+    "rating.suggestion.agent": null,
+    "rating.suggestion.score": null,
+    "status": "completed"
+}
+```
+
+
+### 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
+
+These are some guidelines.
+
+#### 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]