
# Dataset Card for dolly_tutorial

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("nataliaElv/dolly_tutorial")
```

### 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("nataliaElv/dolly_tutorial")
```

### 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| final-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  |
| final-context | 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. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing. |  N/A  |
| final-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. Make sure that the grammar and orthography are correct. |  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, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
        "instruction": "When did Virgin Australia start operating?",
        "response": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route."
    },
    "metadata": null,
    "responses": [
        {
            "status": "submitted",
            "user_id": "dc9c373f-c589-4845-b7e8-890520ca7d43",
            "values": {
                "final-context": {
                    "value": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney."
                },
                "final-instruction": {
                    "value": "When did Virgin Australia start operating?"
                },
                "final-response": {
                    "value": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue."
                }
            }
        }
    ]
}
```

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

```json
{
    "category": "closed_qa",
    "context": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.",
    "external_id": "0",
    "final-context": {
        "status": [
            "submitted"
        ],
        "user_id": [
            "dc9c373f-c589-4845-b7e8-890520ca7d43"
        ],
        "value": [
            "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia\u0027s domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney."
        ]
    },
    "final-instruction": {
        "status": [
            "submitted"
        ],
        "user_id": [
            "dc9c373f-c589-4845-b7e8-890520ca7d43"
        ],
        "value": [
            "When did Virgin Australia start operating?"
        ]
    },
    "final-response": {
        "status": [
            "submitted"
        ],
        "user_id": [
            "dc9c373f-c589-4845-b7e8-890520ca7d43"
        ],
        "value": [
            "Virgin Australia commenced services on 31 August 2000 as Virgin Blue."
        ]
    },
    "instruction": "When did Virgin Australia start operating?",
    "metadata": null,
    "response": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route."
}
```

### 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.
    
    * **final-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) **final-context** 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. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing.".
    * **final-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. Make sure that the grammar and orthography are correct.".

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


# Introduction
In this dataset, you will find a collection of records that show a task category, an instruction, an input and a response. The aim of the project is to correct the instructions, inputs 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. 

# Task categories
Instructions are classified according to 7 possible task categories. Please, read and understand these categories because they will change the way the instruction and input are formulated.  The definitions are based on those made by [Databricks](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm).

## Open / General Q&A
Here you will find an open question, for instance, “Why do people like comedy movies?” or “What is the capital of France?”. In some cases, there’s not a correct answer, and in others, it requires drawing on knowledge of the world at large. This type of task shouldn’t have an input.

## Closed Q&A
These are questions that can be answered using only the information contained in a passage of reference text. For instance, given a paragraph from Wikipedia on the atom, one might ask, “What is the ratio between protons and neutrons in the nucleus?”. In this case, the task should have an instruction (the question), an input (the reference text) and a response.

## Information extraction 
In this task, the instruction will ask to extract entities or other factual information from a passage.  In this case, the task should have an instruction (the question), an input (the reference text) and a response.

## Summarization
Instructions of this kind of task will ask to summarize a passage. The passage should be in the input. The response should be a summarized version of the passage.

## Brainstorming
Brainstorming instructions should ask for open-ended ideation and an associated list of possible options. For instance, “What are some fun activities I can do with my friends this weekend?”.

## Classification
Instructions of this type should ask to make judgments about class membership (e.g. are the items in a list animals, minerals or vegetables) or to judge the properties of a short passage of text, such as the sentiment of a movie review. The item(s) to be classified should appear in the input field. 
 
## Creative writing
Instructions of this class include things like writing a poem or a love letter.


# Questionnaire
To curate the dataset, you will need to provide and answer to the questions below. Please, follow the pointers below to answer each question accordingly. 

If you are not sure about a record and you prefer not to provide a response, click Discard.

## 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. 
- Instructions can contain grammar and orthography errors as long as they are clear.

## 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. 
- Remember to add an input to the tasks that need one: closed Q&A, information extraction, summarization and classification.
- This question should be blank whenever the task doesn’t need one: open / general Q&A, brainstorming, creative writing.
- Inputs can contain grammar and orthography errors as long as they are clear.

## 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 and that it is as complete and concise as possible.
- Responses should have their grammar and orthography checked and correct at all times.



#### 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 dolly\_tutorial
================================


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.


	+ final-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) final-context 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. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing.".
	+ final-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. Make sure that the grammar and orthography are correct.".


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


Introduction
============


In this dataset, you will find a collection of records that show a task category, an instruction, an input and a response. The aim of the project is to correct the instructions, inputs 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.


Task categories
===============


Instructions are classified according to 7 possible task categories. Please, read and understand these categories because they will change the way the instruction and input are formulated. The definitions are based on those made by Databricks.


Open / General Q&A
------------------


Here you will find an open question, for instance, “Why do people like comedy movies?” or “What is the capital of France?”. In some cases, there’s not a correct answer, and in others, it requires drawing on knowledge of the world at large. This type of task shouldn’t have an input.


Closed Q&A
----------


These are questions that can be answered using only the information contained in a passage of reference text. For instance, given a paragraph from Wikipedia on the atom, one might ask, “What is the ratio between protons and neutrons in the nucleus?”. In this case, the task should have an instruction (the question), an input (the reference text) and a response.


Information extraction
----------------------


In this task, the instruction will ask to extract entities or other factual information from a passage. In this case, the task should have an instruction (the question), an input (the reference text) and a response.


Summarization
-------------


Instructions of this kind of task will ask to summarize a passage. The passage should be in the input. The response should be a summarized version of the passage.


Brainstorming
-------------


Brainstorming instructions should ask for open-ended ideation and an associated list of possible options. For instance, “What are some fun activities I can do with my friends this weekend?”.


Classification
--------------


Instructions of this type should ask to make judgments about class membership (e.g. are the items in a list animals, minerals or vegetables) or to judge the properties of a short passage of text, such as the sentiment of a movie review. The item(s) to be classified should appear in the input field.


Creative writing
----------------


Instructions of this class include things like writing a poem or a love letter.


Questionnaire
=============


To curate the dataset, you will need to provide and answer to the questions below. Please, follow the pointers below to answer each question accordingly.


If you are not sure about a record and you prefer not to provide a response, click Discard.


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.
* Instructions can contain grammar and orthography errors as long as they are clear.


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.
* Remember to add an input to the tasks that need one: closed Q&A, information extraction, summarization and classification.
* This question should be blank whenever the task doesn’t need one: open / general Q&A, brainstorming, creative writing.
* Inputs can contain grammar and orthography errors as long as they are clear.


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 and that it is as complete and concise as possible.
* Responses should have their grammar and orthography checked and correct at all times.


#### 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.


	+ final-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) final-context 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. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing.".
	+ final-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. Make sure that the grammar and orthography are correct.".


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


Introduction
============


In this dataset, you will find a collection of records that show a task category, an instruction, an input and a response. The aim of the project is to correct the instructions, inputs 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.


Task categories
===============


Instructions are classified according to 7 possible task categories. Please, read and understand these categories because they will change the way the instruction and input are formulated. The definitions are based on those made by Databricks.


Open / General Q&A
------------------


Here you will find an open question, for instance, “Why do people like comedy movies?” or “What is the capital of France?”. In some cases, there’s not a correct answer, and in others, it requires drawing on knowledge of the world at large. This type of task shouldn’t have an input.


Closed Q&A
----------


These are questions that can be answered using only the information contained in a passage of reference text. For instance, given a paragraph from Wikipedia on the atom, one might ask, “What is the ratio between protons and neutrons in the nucleus?”. In this case, the task should have an instruction (the question), an input (the reference text) and a response.


Information extraction
----------------------


In this task, the instruction will ask to extract entities or other factual information from a passage. In this case, the task should have an instruction (the question), an input (the reference text) and a response.


Summarization
-------------


Instructions of this kind of task will ask to summarize a passage. The passage should be in the input. The response should be a summarized version of the passage.


Brainstorming
-------------


Brainstorming instructions should ask for open-ended ideation and an associated list of possible options. For instance, “What are some fun activities I can do with my friends this weekend?”.


Classification
--------------


Instructions of this type should ask to make judgments about class membership (e.g. are the items in a list animals, minerals or vegetables) or to judge the properties of a short passage of text, such as the sentiment of a movie review. The item(s) to be classified should appear in the input field.


Creative writing
----------------


Instructions of this class include things like writing a poem or a love letter.


Questionnaire
=============


To curate the dataset, you will need to provide and answer to the questions below. Please, follow the pointers below to answer each question accordingly.


If you are not sure about a record and you prefer not to provide a response, click Discard.


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.
* Instructions can contain grammar and orthography errors as long as they are clear.


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.
* Remember to add an input to the tasks that need one: closed Q&A, information extraction, summarization and classification.
* This question should be blank whenever the task doesn’t need one: open / general Q&A, brainstorming, creative writing.
* Inputs can contain grammar and orthography errors as long as they are clear.


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 and that it is as complete and concise as possible.
* Responses should have their grammar and orthography checked and correct at all times. #### 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.


	+ final-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) final-context 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. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing.".
	+ final-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. Make sure that the grammar and orthography are correct.".


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


Introduction
============


In this dataset, you will find a collection of records that show a task category, an instruction, an input and a response. The aim of the project is to correct the instructions, inputs 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.


Task categories
===============


Instructions are classified according to 7 possible task categories. Please, read and understand these categories because they will change the way the instruction and input are formulated. The definitions are based on those made by Databricks.


Open / General Q&A
------------------


Here you will find an open question, for instance, “Why do people like comedy movies?” or “What is the capital of France?”. In some cases, there’s not a correct answer, and in others, it requires drawing on knowledge of the world at large. This type of task shouldn’t have an input.


Closed Q&A
----------


These are questions that can be answered using only the information contained in a passage of reference text. For instance, given a paragraph from Wikipedia on the atom, one might ask, “What is the ratio between protons and neutrons in the nucleus?”. In this case, the task should have an instruction (the question), an input (the reference text) and a response.


Information extraction
----------------------


In this task, the instruction will ask to extract entities or other factual information from a passage. In this case, the task should have an instruction (the question), an input (the reference text) and a response.


Summarization
-------------


Instructions of this kind of task will ask to summarize a passage. The passage should be in the input. The response should be a summarized version of the passage.


Brainstorming
-------------


Brainstorming instructions should ask for open-ended ideation and an associated list of possible options. For instance, “What are some fun activities I can do with my friends this weekend?”.


Classification
--------------


Instructions of this type should ask to make judgments about class membership (e.g. are the items in a list animals, minerals or vegetables) or to judge the properties of a short passage of text, such as the sentiment of a movie review. The item(s) to be classified should appear in the input field.


Creative writing
----------------


Instructions of this class include things like writing a poem or a love letter.


Questionnaire
=============


To curate the dataset, you will need to provide and answer to the questions below. Please, follow the pointers below to answer each question accordingly.


If you are not sure about a record and you prefer not to provide a response, click Discard.


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.
* Instructions can contain grammar and orthography errors as long as they are clear.


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.
* Remember to add an input to the tasks that need one: closed Q&A, information extraction, summarization and classification.
* This question should be blank whenever the task doesn’t need one: open / general Q&A, brainstorming, creative writing.
* Inputs can contain grammar and orthography errors as long as they are clear.


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 and that it is as complete and concise as possible.
* Responses should have their grammar and orthography checked and correct at all times. #### 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.


	+ final-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) final-context 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. Leave this question empty in the case of these task categories: open / general Q&A, brainstorming, creative writing.".
	+ final-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. Make sure that the grammar and orthography are correct.".


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