size_categories:n<1K format:parquet modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars library:argilla region:us rlfh argilla human-feedback
---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for mistral-vs-llama-rating

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("argilla/mistral-vs-llama-rating")
```

### 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("argilla/mistral-vs-llama-rating")
```

### Supported Tasks and Leaderboards

This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) 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**, and **guidelines**.

The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| input | Input | TextField | True | False |
| response_1 | Response_1 | TextField | True | True |
| response_2 | Response_2 | TextField | True | True |


The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| rating_1 | Rate the response_1? | RatingQuestion | True | N/A | [1, 2, 3, 4, 5] |
| rationale_1 | Rationale behind response_1's ranking? | TextQuestion | True | N/A | N/A |
| rating_2 | Rate the response_2? | RatingQuestion | True | N/A | [1, 2, 3, 4, 5] |
| rationale_2 | Rationale behind response_2's ranking? | TextQuestion | True | N/A | N/A |


**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, 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.

Finally, the **guidelines** 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
{
    "fields": {
        "input": "How do I locate my card?",
        "response_1": " Thank you for reaching out! I\u0027m here to help you with your query. However, I must point out that the term \"locate\" can have different meanings depending on the context. Could you please provide more details or clarify what you mean by \"locate\"? Are you trying to find a specific card, such as a credit card or debit card? Or perhaps you\u0027re looking for something else entirely? Once I have a better understanding of your question, I\u0027ll be happy to assist you in a responsible and accurate manner.",
        "response_2": " Of course! I\u0027d be happy to help you locate your missing card. To start, could you please tell me more about the card you\u0027re trying to find? Is it a credit card, debit card, or another type of card? Additionally, can you provide more details about the circumstances surrounding its disappearance? For example, did you lose it at home, in a store, or somewhere else? Any additional information you can provide will help me give you more tailored advice on how to locate your card."
    },
    "metadata": {},
    "responses": [
        {
            "status": "submitted",
            "user_id": "39bdc103-0e37-4515-a353-abe3b68bdebd",
            "values": {
                "rating_1": {
                    "value": 3
                },
                "rating_2": {
                    "value": 4
                },
                "rationale_1": {
                    "value": "The text is correct and provides a helpful response to the query. However, it lacks specific instructions or steps to help the user locate their card, hence it\u0027s not highly informative."
                },
                "rationale_2": {
                    "value": "The text is accurate and provides a more detailed response to the query. It asks for more specific information to provide a tailored solution, making it highly informative. However, it still lacks the profound insights or comprehensive information that would make it outstandingly helpful."
                }
            }
        }
    ],
    "suggestions": [
        {
            "agent": "gpt-4",
            "question_id": "0a3cf946-2bd3-4d57-b42c-caddf8cc4979",
            "question_name": "rating_1",
            "value": 3
        },
        {
            "agent": "gpt-4",
            "question_id": "d7d8347b-554a-4eaf-ad2b-64898f3e0841",
            "question_name": "rationale_1",
            "value": "The text is correct and provides a helpful response to the query. However, it lacks specific instructions or steps to help the user locate their card, hence it\u0027s not highly informative."
        },
        {
            "agent": "gpt-4",
            "question_id": "99672a09-0d1c-45d7-ac2c-fe692603c5b4",
            "question_name": "rating_2",
            "value": 4
        },
        {
            "agent": "gpt-4",
            "question_id": "8276bff4-1106-4fef-abcb-ebc70beb791e",
            "question_name": "rationale_2",
            "value": "The text is accurate and provides a more detailed response to the query. It asks for more specific information to provide a tailored solution, making it highly informative. However, it still lacks the profound insights or comprehensive information that would make it outstandingly helpful."
        }
    ]
}
```

While the same record in HuggingFace `datasets` looks as follows:

```json
{
    "external_id": null,
    "input": "How do I locate my card?",
    "metadata": "{}",
    "rating_1": [
        {
            "status": "submitted",
            "user_id": "39bdc103-0e37-4515-a353-abe3b68bdebd",
            "value": 3
        }
    ],
    "rating_1-suggestion": 3,
    "rating_1-suggestion-metadata": {
        "agent": "gpt-4",
        "score": null,
        "type": null
    },
    "rating_2": [
        {
            "status": "submitted",
            "user_id": "39bdc103-0e37-4515-a353-abe3b68bdebd",
            "value": 4
        }
    ],
    "rating_2-suggestion": 4,
    "rating_2-suggestion-metadata": {
        "agent": "gpt-4",
        "score": null,
        "type": null
    },
    "rationale_1": [
        {
            "status": "submitted",
            "user_id": "39bdc103-0e37-4515-a353-abe3b68bdebd",
            "value": "The text is correct and provides a helpful response to the query. However, it lacks specific instructions or steps to help the user locate their card, hence it\u0027s not highly informative."
        }
    ],
    "rationale_1-suggestion": "The text is correct and provides a helpful response to the query. However, it lacks specific instructions or steps to help the user locate their card, hence it\u0027s not highly informative.",
    "rationale_1-suggestion-metadata": {
        "agent": "gpt-4",
        "score": null,
        "type": null
    },
    "rationale_2": [
        {
            "status": "submitted",
            "user_id": "39bdc103-0e37-4515-a353-abe3b68bdebd",
            "value": "The text is accurate and provides a more detailed response to the query. It asks for more specific information to provide a tailored solution, making it highly informative. However, it still lacks the profound insights or comprehensive information that would make it outstandingly helpful."
        }
    ],
    "rationale_2-suggestion": "The text is accurate and provides a more detailed response to the query. It asks for more specific information to provide a tailored solution, making it highly informative. However, it still lacks the profound insights or comprehensive information that would make it outstandingly helpful.",
    "rationale_2-suggestion-metadata": {
        "agent": "gpt-4",
        "score": null,
        "type": null
    },
    "response_1": " Thank you for reaching out! I\u0027m here to help you with your query. However, I must point out that the term \"locate\" can have different meanings depending on the context. Could you please provide more details or clarify what you mean by \"locate\"? Are you trying to find a specific card, such as a credit card or debit card? Or perhaps you\u0027re looking for something else entirely? Once I have a better understanding of your question, I\u0027ll be happy to assist you in a responsible and accurate manner.",
    "response_2": " Of course! I\u0027d be happy to help you locate your missing card. To start, could you please tell me more about the card you\u0027re trying to find? Is it a credit card, debit card, or another type of card? Additionally, can you provide more details about the circumstances surrounding its disappearance? For example, did you lose it at home, in a store, or somewhere else? Any additional information you can provide will help me give you more tailored advice on how to locate your card."
}
```

### 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 suppported. These are the ones that will be used to provide responses to the questions.
    
    * **input** is of type `TextField`.
    * **response_1** is of type `TextField`.
    * **response_2** is of type `TextField`.

* **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`.
    
    * **rating_1** is of type `RatingQuestion` with the following allowed values [1, 2, 3, 4, 5].
    * **rationale_1** is of type `TextQuestion`.
    * **rating_2** is of type `RatingQuestion` with the following allowed values [1, 2, 3, 4, 5].
    * **rationale_2** is of type `TextQuestion`.

* **✨ NEW** **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) **rating_1-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5].
    * (optional) **rationale_1-suggestion** is of type `text`.
    * (optional) **rating_2-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5].
    * (optional) **rationale_2-suggestion** is of type `text`.

Additionally, we also have one more field which is optional and is the following:

* **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

[More Information Needed]

#### 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]