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README.md

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 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-    id: field
-  - name: label
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      sequence: string
-      id: question
-    - name: status
-      dtype: string
-      id: question
-  - name: label-suggestion
-    sequence: string
-    id: suggestion
-  - name: label-suggestion-metadata
-    struct:
-    - name: type
-      dtype: string
-      id: suggestion-metadata
-    - name: score
-      sequence:
-        dtype: float32
-        id: suggestion-metadata
-    - name: agent
-      dtype: string
-      id: suggestion-metadata
-  - name: external_id
-    dtype: string
-    id: external_id
-  - name: metadata
-    dtype: string
-    id: metadata
-  splits:
-  - name: train
-    num_bytes: 2173729
-    num_examples: 1559
-  download_size: 1099549
-  dataset_size: 2173729
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+size_categories: 1K<n<10K
+tags:
+- rlfh
+- argilla
+- human-feedback
 ---
+
+# Dataset Card for argilla-contextual-ai
+
+This dataset has been created with [Argilla](https://docs.argilla.io).
+
+As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
+
+## Dataset Description
+
+- **Homepage:** https://argilla.io
+- **Repository:** https://github.com/argilla-io/argilla
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This dataset contains:
+
+* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
+
+* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` 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.
+
+### Load 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.FeedbackDataset.from_huggingface("dannymartin/argilla-contextual-ai")
+```
+
+### Load with `datasets`
+
+To load 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("dannymartin/argilla-contextual-ai")
+```
+
+### Supported Tasks and Leaderboards
+
+This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
+
+There are no leaderboards associated with this dataset.
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data in Argilla
+
+The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
+
+The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
+
+| Field Name | Title | Type | Required | Markdown |
+| ---------- | ----- | ---- | -------- | -------- |
+| text | Text | text | True | False |
+
+
+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 |
+| ------------- | ----- | ---- | -------- | ----------- | ------------- |
+| label | Label | multi_label_selection | True | Classify the text by selecting the correct label from the given list of labels. | ['Baking', 'Grilling', 'Slow cooking', 'Pressure cooking', 'Air-fryer', 'Smoking', 'Pickling', 'Roasting', 'Steaming', 'Boiling', 'Sautéing', 'Sous vide', 'Stir-frying'] |
+
+
+The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
+
+The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
+
+
+
+| Metadata Name | Title | Type | Values | Visible for Annotators |
+| ------------- | ----- | ---- | ------ | ---------------------- |
+
+
+The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
+
+### Data Instances
+
+An example of a dataset instance in Argilla looks as follows:
+
+```json
+{
+    "external_id": "record-0",
+    "fields": {
+        "text": "Recipe: Zinger Green Tea Drink\nDescription: I got an email from Biggest Loser with this recipe on it.  \r\nThis is refreshing drink that\u0027s perfect for spring. Packed with antioxidants, this tea is bursting with flavor, too. Fresh lime juice gives it a tangy zing and a wallop of vitamin C.\nIngredients: [{\"text\": \"6 cups water\"}, {\"text\": \"1 cup firmly packed fresh mint leaves\"}, {\"text\": \"3 green tea bags\"}, {\"text\": \"1 1/3 cups agave nectar\"}, {\"text\": \"1 1/3 cups fresh lime juice\"}, {\"text\": \"6 lime slices, for garnish\"}]\nInstructions: Bring the water to boil in a 3-quart saucepan. Add the mint and tea bags, remove from the heat, and let steep for 5 minutes. \r\nStrain.\r\nStir in the agave and lime juice. \r\nServe hot or iced, garnished with the lime slices. \r\nmakes 11.2 quarts.\n"
+    },
+    "metadata": {},
+    "responses": [],
+    "suggestions": [],
+    "vectors": {}
+}
+```
+
+While the same record in HuggingFace `datasets` looks as follows:
+
+```json
+{
+    "external_id": "record-0",
+    "label": [],
+    "label-suggestion": null,
+    "label-suggestion-metadata": {
+        "agent": null,
+        "score": null,
+        "type": null
+    },
+    "metadata": "{}",
+    "text": "Recipe: Zinger Green Tea Drink\nDescription: I got an email from Biggest Loser with this recipe on it.  \r\nThis is refreshing drink that\u0027s perfect for spring. Packed with antioxidants, this tea is bursting with flavor, too. Fresh lime juice gives it a tangy zing and a wallop of vitamin C.\nIngredients: [{\"text\": \"6 cups water\"}, {\"text\": \"1 cup firmly packed fresh mint leaves\"}, {\"text\": \"3 green tea bags\"}, {\"text\": \"1 1/3 cups agave nectar\"}, {\"text\": \"1 1/3 cups fresh lime juice\"}, {\"text\": \"6 lime slices, for garnish\"}]\nInstructions: Bring the water to boil in a 3-quart saucepan. Add the mint and tea bags, remove from the heat, and let steep for 5 minutes. \r\nStrain.\r\nStir in the agave and lime juice. \r\nServe hot or iced, garnished with the lime slices. \r\nmakes 11.2 quarts.\n"
+}
+```
+
+### Data Fields
+
+Among the dataset fields, we differentiate between the following:
+
+* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
+    
+    * **text** is of type `text`.
+
+* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
+    
+    * **label** is of type `multi_label_selection` with the following allowed values ['Baking', 'Grilling', 'Slow cooking', 'Pressure cooking', 'Air-fryer', 'Smoking', 'Pickling', 'Roasting', 'Steaming', 'Boiling', 'Sautéing', 'Sous vide', 'Stir-frying'], and description "Classify the text by selecting the correct label from the given list of labels.".
+
+* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
+    
+    * (optional) **label-suggestion** is of type `multi_label_selection` with the following allowed values ['Baking', 'Grilling', 'Slow cooking', 'Pressure cooking', 'Air-fryer', 'Smoking', 'Pickling', 'Roasting', 'Steaming', 'Boiling', 'Sautéing', 'Sous vide', 'Stir-frying'].
+
+
+
+Additionally, we also have two more fields that are optional and are the following:
+
+* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
+* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
+
+### 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
+
+This is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection.
+
+#### 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]