dvilasuero/intel-orca-dpo-pair-rg

---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for intel-orca-dpo-pair-rg

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("dvilasuero/intel-orca-dpo-pair-rg")
```

### 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("dvilasuero/intel-orca-dpo-pair-rg")
```

### 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 |
| ---------- | ----- | ---- | -------- | -------- |
| input | input | text | True | True |
| generations-1 | generations-1 | text | True | True |
| generations-2 | generations-2 | text | True | True |

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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| generations-1-rating | What's the rating for generations-1? | rating | True | N/A | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| generations-2-rating | What's the rating for generations-2? | rating | True | N/A | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| rating-rationale | What's the rationale behind each rating? | text | True | N/A | N/A |

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 |
| ------------- | ----- | ---- | ------ | ---------------------- |
| length-input | length-input | integer | None - None | True |
| length-generations-1 | length-generations-1 | integer | None - None | True |
| rating-generations-1 | rating-generations-1 | float | None - None | True |
| length-generations-2 | length-generations-2 | integer | None - None | True |
| rating-generations-2 | rating-generations-2 | float | None - None | True |
| distance-best-rating | distance-best-rating | float | None - None | True |

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": null,
"fields": {
"generations-1": "[\n [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n]",
"generations-2": "Sure, I\u0027d be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.",
"input": "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)\u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:"
},
"metadata": {
"distance-best-rating": 0.0,
"labelling-model": "gpt-4-1106-preview",
"length-generations-1": 131,
"length-generations-2": 731,
"length-input": 718,
"rating-generations-1": 9.0,
"rating-generations-2": 9.0
},
"responses": [],
"suggestions": [
{
"agent": null,
"question_name": "rating-rationale",
"score": null,
"type": null,
"value": "\nBoth Assistant 1 and Assistant 2 provided correct RDF triplets for the given sentence. Both assistants used a format that accurately represents the relationships present in the sentence with minor differences in the stylistic representation of the predicate.\n\nAssistant 1 used the natural language format for predicates, stating \"has ground\" and \"plays at\", which clearly aligns with the typical RDF representation where the predicate tries to be a URI that is more formal. However, since the task prompt doesn\u0027t specify a requirement for the predicates to be in URI form, this representation is acceptable, especially considering human readability.\n\nAssistant 2 transformed the predicates into a more formal-looking format by using camel case (hasGround, playsAt), which may suggest a transition towards a URI, although they are not provided as such. This is a common practice when designing RDF predicates, intending to align with web standards, although again, the task did not explicitly require this form.\n\nBoth assistants explained the relationships captured by the triplets, which is helpful for understanding how the RDF structure relates to the original sentence. There are no factual inaccuracies in either output, and both sets of triplets are equivalent in terms of the information they represent.\n\nOverall, the level of detail was similar, with each assistant providing a brief explanation following their respective triplets. Neither output contained unnecessary or irrelevant information, and no critical information was missing. \n\nBoth assistants would have received a score of 10 if the predicates were provided in a format that hinted at being URIs (e.g., prefixed with a namespace or in a full URI format), which is the more standard and formal practice for RDF predicates. Nevertheless, the assistants\u0027 performance was high given the context of the question, which did not specify this requirement. Therefore, both receive a score of 9."
},
{
"agent": null,
"question_name": "generations-1-rating",
"score": null,
"type": null,
"value": 9
},
{
"agent": null,
"question_name": "generations-2-rating",
"score": null,
"type": null,
"value": 9
}
],
"vectors": {}
}
```

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

```json
{
"external_id": null,
"generations-1": "[\n [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n]",
"generations-1-rating": [],
"generations-1-rating-suggestion": 9,
"generations-1-rating-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"generations-2": "Sure, I\u0027d be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.",
"generations-2-rating": [],
"generations-2-rating-suggestion": 9,
"generations-2-rating-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"input": "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)\u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:",
"metadata": "{\"length-input\": 718, \"length-generations-1\": 131, \"length-generations-2\": 731, \"rating-generations-1\": 9.0, \"rating-generations-2\": 9.0, \"distance-best-rating\": 0.0, \"labelling-model\": \"gpt-4-1106-preview\"}",
"rating-rationale": [],
"rating-rationale-suggestion": "\nBoth Assistant 1 and Assistant 2 provided correct RDF triplets for the given sentence. Both assistants used a format that accurately represents the relationships present in the sentence with minor differences in the stylistic representation of the predicate.\n\nAssistant 1 used the natural language format for predicates, stating \"has ground\" and \"plays at\", which clearly aligns with the typical RDF representation where the predicate tries to be a URI that is more formal. However, since the task prompt doesn\u0027t specify a requirement for the predicates to be in URI form, this representation is acceptable, especially considering human readability.\n\nAssistant 2 transformed the predicates into a more formal-looking format by using camel case (hasGround, playsAt), which may suggest a transition towards a URI, although they are not provided as such. This is a common practice when designing RDF predicates, intending to align with web standards, although again, the task did not explicitly require this form.\n\nBoth assistants explained the relationships captured by the triplets, which is helpful for understanding how the RDF structure relates to the original sentence. There are no factual inaccuracies in either output, and both sets of triplets are equivalent in terms of the information they represent.\n\nOverall, the level of detail was similar, with each assistant providing a brief explanation following their respective triplets. Neither output contained unnecessary or irrelevant information, and no critical information was missing. \n\nBoth assistants would have received a score of 10 if the predicates were provided in a format that hinted at being URIs (e.g., prefixed with a namespace or in a full URI format), which is the more standard and formal practice for RDF predicates. Nevertheless, the assistants\u0027 performance was high given the context of the question, which did not specify this requirement. Therefore, both receive a score of 9.",
"rating-rationale-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
```

### 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.

* **input** is of type `text`.
* **generations-1** is of type `text`.
* **generations-2** 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`.

* **generations-1-rating** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
* **generations-2-rating** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
* **rating-rationale** is of type `text`.

* **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) **generations-1-rating-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
* (optional) **generations-2-rating-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
* (optional) **rating-rationale-suggestion** is of type `text`.

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

[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]

原始信息汇总

数据集卡片 for intel-orca-dpo-pair-rg

数据集描述

• 数据集摘要
  • 该数据集包含以下内容：
    • 符合Argilla数据集格式的配置文件argilla.yaml。
    • 兼容HuggingFace datasets格式的数据记录。
    • 用于构建和整理数据集的注释指南（如果已在Argilla中定义）。

数据集结构

数据字段

• 字段（Fields）

  • input: 类型为text。
  • generations-1: 类型为text。
  • generations-2: 类型为text。

• 问题（Questions）

  • generations-1-rating: 类型为rating，允许值为[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]。
  • generations-2-rating: 类型为rating，允许值为[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]。
  • rating-rationale: 类型为text。

• 建议（Suggestions）

  • generations-1-rating-suggestion: 类型为rating，允许值为[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]（可选）。
  • generations-2-rating-suggestion: 类型为rating，允许值为[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]（可选）。
  • rating-rationale-suggestion: 类型为text（可选）。

• 元数据（Metadata）

  • 可选字段，用于提供数据记录的额外信息。

• 外部ID（external_id）

  • 可选字段，用于提供数据记录的外部ID。

数据实例

• Argilla格式 json { "external_id": null, "fields": { "generations-1": "[ ["AFC Ajax (amateurs)", "has ground", "Sportpark De Toekomst"], ["Ajax Youth Academy", "plays at", "Sportpark De Toekomst"] ]", "generations-2": "Sure, Iu0027d be happy to help! Here are the RDF triplets for the input sentence:

[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst] [Ajax Youth Academy, playsAt, Sportpark De Toekomst]

Explanation:

• AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.
• Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.

Note that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.", "input": "You will be given a definition of a task first, then some input of the task. This task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.

AFC Ajax (amateurs)u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play. Output:" }, "metadata": { "distance-best-rating": 0.0, "labelling-model": "gpt-4-1106-preview", "length-generations-1": 131, "length-generations-2": 731, "length-input": 718, "rating-generations-1": 9.0, "rating-generations-2": 9.0 }, "responses": [], "suggestions": [ { "agent": null, "question_name": "rating-rationale", "score": null, "type": null, "value": " Both Assistant 1 and Assistant 2 provided correct RDF triplets for the given sentence. Both assistants used a format that accurately represents the relationships present in the sentence with minor differences in the stylistic representation of the predicate.

Assistant 1 used the natural language format for predicates, stating "has ground" and "plays at", which clearly aligns with the typical RDF representation where the predicate tries to be a URI that is more formal. However, since the task prompt doesnu0027t specify a requirement for the predicates to be in URI form, this representation is acceptable, especially considering human readability.

Assistant 2 transformed the predicates into a more formal-looking format by using camel case (hasGround, playsAt), which may suggest a transition towards a URI, although they are not provided as such. This is a common practice when designing RDF predicates, intending to align with web standards, although again, the task did not explicitly require this form.

Both assistants explained the relationships captured by the triplets, which is helpful for understanding how the RDF structure relates to the original sentence. There are no factual inaccuracies in either output, and both sets of triplets are equivalent in terms of the information they represent.

Overall, the level of detail was similar, with each assistant providing a brief explanation following their respective triplets. Neither output contained unnecessary or irrelevant information, and no critical information was missing.

Both assistants would have received a score of 10 if the predicates were provided in a format that hinted at being URIs (e.g., prefixed with a namespace or in a full URI format), which is the more standard and formal practice for RDF predicates. Nevertheless, the assistantsu0027 performance was high given the context of the question, which did not specify this requirement. Therefore, both receive a score of 9." }, { "agent": null, "question_name": "generations-1-rating", "score": null, "type": null, "value": 9 }, { "agent": null, "question_name": "generations-2-rating", "score": null, "type": null, "value": 9 } ], "vectors": {} }

• HuggingFace datasets格式 json { "external_id": null, "generations-1": "[ ["AFC Ajax (amateurs)", "has ground", "Sportpark De Toekomst"], ["Ajax Youth Academy", "plays at", "Sportpark De Toekomst"] ]", "generations-1-rating": [], "generations-1-rating-suggestion": 9, "generations-1-rating-suggestion-metadata": { "agent": null, "score": null, "type": null }, "generations-2": "Sure, Iu0027d be happy to help! Here are the RDF triplets for the input sentence:

[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst] [Ajax Youth Academy, playsAt, Sportpark De Toekomst]

Explanation:

• AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.
• Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.

Note that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.", "generations-2-rating": [], "generations-2-rating-suggestion": 9, "generations-2-rating-suggestion-metadata": { "agent": null, "score": null, "type": null }, "input": "You will be given a definition of a task first, then some input of the task. This task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.

AFC Ajax (amateurs)u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play. Output:", "metadata": "{"length-input": 718, "length-generations-1": 131, "length-generations-2": 731, "rating-generations-1": 9.0, "rating-generations-2": 9.0, "distance-best-rating": 0.0, "labelling-model": "gpt-4-1106-preview"}", "rating-rationale": [], "rating-rationale-suggestion": " Both Assistant 1 and Assistant 2 provided correct RDF triplets for the given sentence. Both assistants used a format that accurately represents the relationships present in the sentence with minor differences in the stylistic representation of the predicate.

Assistant 1 used the natural language format for predicates, stating "has ground" and "plays at", which clearly aligns with the typical RDF representation where the predicate tries to be a URI that is more formal. However, since the task prompt doesnu0027t specify a requirement for the predicates to be in URI form, this representation is acceptable, especially considering human readability.

Assistant 2 transformed the predicates into a more formal-looking format by using camel case (hasGround, playsAt), which may suggest a transition towards a URI, although they are not provided as such. This is a common practice when designing RDF predicates, intending to align with web standards, although again, the task did not explicitly require this form.

Both assistants explained the relationships captured by the triplets, which is helpful for understanding how the RDF structure relates to the original sentence. There are no factual inaccuracies in either output, and both sets of triplets are equivalent in terms of the information they represent.

Overall, the level of detail was similar, with each assistant providing a brief explanation following their respective triplets. Neither output contained unnecessary or irrelevant information, and no critical information was missing.

Both assistants would have received a score of 10 if the predicates were provided in a format that hinted at being URIs (e.g., prefixed with a namespace or in a full URI format), which is the more standard and formal practice for RDF predicates. Nevertheless, the assistantsu0027 performance was high given the context of the question, which did not specify this requirement. Therefore, both receive a score of 9.", "rating-rationale-suggestion-metadata": { "agent": null, "score": null, "type": null } }

数据集创建

数据分割

• 数据集包含一个分割：train。