
# Dataset Card for databricks-dolly-15k-curated-es

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.cfg`. 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("mariagrandury/databricks-dolly-15k-curated-es")
```

### 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("mariagrandury/databricks-dolly-15k-curated-es")
```

### 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**, 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 |
| ---------- | ----- | ---- | -------- | -------- |
| category | Task category | TextField | True | False |
| instruction | Instruction | TextField | True | False |
| context | Input | TextField | True | False |
| response | Response | TextField | True | False |


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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| new-instruction | Final instruction: | TextQuestion | True | Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here. |  N/A  |
| new-input | Final input: | TextQuestion | True | Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty. |  N/A  |
| new-response | Final response: | TextQuestion | True | Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here. |  N/A  |


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
{
    "external_id": "0",
    "fields": {
        "category": "closed_qa",
        "context": "Virgin Australia, nombre comercial de Virgin Australia Airlines Pty Ltd, es una compa\u00f1\u00eda a\u00e9rea con sede en Australia. Es la mayor aerol\u00ednea por tama\u00f1o de flota que utiliza la marca Virgin. Inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una \u00fanica ruta. Se encontr\u00f3 de repente como una importante aerol\u00ednea en el mercado nacional australiano tras la quiebra de Ansett Australia en septiembre de 2001. Desde entonces, la aerol\u00ednea ha crecido hasta prestar servicio directo a 32 ciudades de Australia, desde los centros de Brisbane, Melbourne y Sydney.",
        "instruction": "\u00bfCu\u00e1ndo empez\u00f3 a operar Virgin Australia?",
        "response": "Virgin Australia inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una sola ruta."
    },
    "metadata": null,
    "responses": []
}
```

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

```json
{
    "category": "closed_qa",
    "context": "Virgin Australia, nombre comercial de Virgin Australia Airlines Pty Ltd, es una compa\u00f1\u00eda a\u00e9rea con sede en Australia. Es la mayor aerol\u00ednea por tama\u00f1o de flota que utiliza la marca Virgin. Inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una \u00fanica ruta. Se encontr\u00f3 de repente como una importante aerol\u00ednea en el mercado nacional australiano tras la quiebra de Ansett Australia en septiembre de 2001. Desde entonces, la aerol\u00ednea ha crecido hasta prestar servicio directo a 32 ciudades de Australia, desde los centros de Brisbane, Melbourne y Sydney.",
    "external_id": "0",
    "instruction": "\u00bfCu\u00e1ndo empez\u00f3 a operar Virgin Australia?",
    "metadata": null,
    "new-input": null,
    "new-instruction": null,
    "new-response": null,
    "response": "Virgin Australia inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una sola ruta."
}
```

### 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.
    
    * **category** is of type `TextField`.
    * **instruction** is of type `TextField`.
    * (optional) **context** is of type `TextField`.
    * **response** is of type `TextField`.

* **Questions:** These 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.
    
    * **new-instruction** is of type `TextQuestion`, and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
    * (optional) **new-input** is of type `TextQuestion`, and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.".
    * **new-response** is of type `TextQuestion`, and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.".

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

In this dataset, you will find a collection of records that show a category, an instruction, an input and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.

To curate the dataset, you will need to provide an answer to the following text fields:

1 - Final instruction:
The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.

2 - Final input:
The final version of the instruction field. You may copy it using the copy icon in the input field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an input to be completed, leave this question blank.

3 - Final response:
The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.

You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard.

#### 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]
size_categories:10K<n<100K rlfh argilla human-feedback region:us
{"size_categories": "10K<n<100K", "tags": ["rlfh", "argilla", "human-feedback"]}
TAGS
#size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us 

Dataset Card for databricks-dolly-15k-curated-es
================================================


This dataset has been created with Argilla.


As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'.


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact:


### Dataset Summary


This dataset contains:


* A dataset configuration file conforming to the Argilla dataset format named 'URL'. 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 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:


### 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:


### Supported Tasks and Leaderboards


This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.


There are no leaderboards associated with this dataset.


### Languages


Dataset Structure
-----------------


### Data in Argilla


The dataset is created in Argilla with: fields, questions, 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.



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.



Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.


### Data Instances


An example of a dataset instance in Argilla looks as follows:


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


### 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.


	+ category is of type 'TextField'.
	+ instruction is of type 'TextField'.
	+ (optional) context is of type 'TextField'.
	+ response is of type 'TextField'.
* Questions: These 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.


	+ new-instruction is of type 'TextQuestion', and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
	+ (optional) new-input is of type 'TextQuestion', and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.".
	+ new-response is of type 'TextQuestion', and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.".


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


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation guidelines


In this dataset, you will find a collection of records that show a category, an instruction, an input and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.


To curate the dataset, you will need to provide an answer to the following text fields:


1 - Final instruction:
The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.


2 - Final input:
The final version of the instruction field. You may copy it using the copy icon in the input field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an input to be completed, leave this question blank.


3 - Final response:
The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.


You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard.


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions



### Dataset Summary


This dataset contains:


* A dataset configuration file conforming to the Argilla dataset format named 'URL'. 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 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: ### 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: ### Supported Tasks and Leaderboards


This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.


There are no leaderboards associated with this dataset. ### Languages


Dataset Structure
----------------- ### Data in Argilla


The dataset is created in Argilla with: fields, questions, 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.



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.



Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. ### Data Instances


An example of a dataset instance in Argilla looks as follows:


While the same record in HuggingFace 'datasets' looks as follows: ### 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.


	+ category is of type 'TextField'.
	+ instruction is of type 'TextField'.
	+ (optional) context is of type 'TextField'.
	+ response is of type 'TextField'.
* Questions: These 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.


	+ new-instruction is of type 'TextQuestion', and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
	+ (optional) new-input is of type 'TextQuestion', and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.".
	+ new-response is of type 'TextQuestion', and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.".


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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation guidelines


In this dataset, you will find a collection of records that show a category, an instruction, an input and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.


To curate the dataset, you will need to provide an answer to the following text fields:


1 - Final instruction:
The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.


2 - Final input:
The final version of the instruction field. You may copy it using the copy icon in the input field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an input to be completed, leave this question blank.


3 - Final response:
The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.


You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us 
 ### Dataset Summary


This dataset contains:


* A dataset configuration file conforming to the Argilla dataset format named 'URL'. 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 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: ### 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: ### Supported Tasks and Leaderboards


This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.


There are no leaderboards associated with this dataset. ### Languages


Dataset Structure
----------------- ### Data in Argilla


The dataset is created in Argilla with: fields, questions, 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.



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.



Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. ### Data Instances


An example of a dataset instance in Argilla looks as follows:


While the same record in HuggingFace 'datasets' looks as follows: ### 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.


	+ category is of type 'TextField'.
	+ instruction is of type 'TextField'.
	+ (optional) context is of type 'TextField'.
	+ response is of type 'TextField'.
* Questions: These 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.


	+ new-instruction is of type 'TextQuestion', and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
	+ (optional) new-input is of type 'TextQuestion', and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.".
	+ new-response is of type 'TextQuestion', and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.".


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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation guidelines


In this dataset, you will find a collection of records that show a category, an instruction, an input and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.


To curate the dataset, you will need to provide an answer to the following text fields:


1 - Final instruction:
The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.


2 - Final input:
The final version of the instruction field. You may copy it using the copy icon in the input field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an input to be completed, leave this question blank.


3 - Final response:
The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.


You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions
passage: TAGS
#size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us 
### Dataset Summary


This dataset contains:


* A dataset configuration file conforming to the Argilla dataset format named 'URL'. 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 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:### 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:### Supported Tasks and Leaderboards


This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.


There are no leaderboards associated with this dataset.### Languages


Dataset Structure
-----------------### Data in Argilla


The dataset is created in Argilla with: fields, questions, 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.



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.



Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. passage: ### Data Instances


An example of a dataset instance in Argilla looks as follows:


While the same record in HuggingFace 'datasets' looks as follows:### 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.


	+ category is of type 'TextField'.
	+ instruction is of type 'TextField'.
	+ (optional) context is of type 'TextField'.
	+ response is of type 'TextField'.
* Questions: These 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.


	+ new-instruction is of type 'TextQuestion', and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
	+ (optional) new-input is of type 'TextQuestion', and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.".
	+ new-response is of type 'TextQuestion', and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.".


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### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations