
# Dataset Card for temp

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("frank098/temp")
```

### 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("frank098/temp")
```

### 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 |
| ---------- | ----- | ---- | -------- | -------- |
| prompt | Prompt | TextField | True | False |
| answer1 | Answer1 | TextField | True | False |
| answer2 | Answer2 | 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 |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| relevant | Which of the two responses most accurately completes the instruction? | LabelQuestion | True | N/A |  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": null,
    "fields": {
        "answer1": "To determine if a prefix is reachable, BGP performs the following initial check:\n\n1. It checks if there is an existing route for the prefix in its routing table.\n2. If there is no existing route, it checks if the prefix is within its own IP address range.\n3. If the prefix is within the IP address range, BGP considers it reachable and advertises it to its neighboring routers.\n4. If the prefix is not within the IP address range, BGP considers it unreachable and does not advertise it to its neighboring routers.\n\nNote: This answer assumes that the user is familiar with BGP (Border Gateway Protocol) and may need additional explanation for clarification.\u003c/s\u003e",
        "answer2": "set forwarding-options sampling family inet filter s1 term 1 then {\n    destination {\n        ip;\n    }\n    next-hop;\n    protocol;\n    port;\n    loss-priority low;\n    output-node 192.168.10.100;\n    flow-server 192.168.10.100 8888;\n    sampling-action random-detect 0.1;\n}\n\nThis Junos command sets up IPv4 traffic sampling on the router with instance s1 and sends the sampled data to the flow-server with the IP address 192.168.10.100 using port number 8888. The sampling action is set to random-detect with a drop probability of 0.1.\n\nNote: Make sure to replace the IP addresses and port numbers with the appropriate values for your network setup.\n\nPlease let me know if you need any further assistance.\u003c/s\u003e",
        "prompt": "\nYou, as a helpful AI assistant, will provide a detailed, step-by-step answer to the user\u0027s question.\n\nUSER: Create a Junos command that configures the router to enable IPv4 traffic sampling with instance s1, and directs the sampled data to be sent to the flow-server with the IP address 198.51.100.2 on port 2055.\n\nASSISTANT: \n"
    },
    "metadata": null,
    "responses": [
        {
            "status": "submitted",
            "user_id": "c8f4058e-7b43-43b2-ae9d-4dc8da3fc138",
            "values": {
                "relevant": {
                    "value": "answer1"
                }
            }
        }
    ]
}
```

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

```json
{
    "answer1": "To determine if a prefix is reachable, BGP performs the following initial check:\n\n1. It checks if there is an existing route for the prefix in its routing table.\n2. If there is no existing route, it checks if the prefix is within its own IP address range.\n3. If the prefix is within the IP address range, BGP considers it reachable and advertises it to its neighboring routers.\n4. If the prefix is not within the IP address range, BGP considers it unreachable and does not advertise it to its neighboring routers.\n\nNote: This answer assumes that the user is familiar with BGP (Border Gateway Protocol) and may need additional explanation for clarification.\u003c/s\u003e",
    "answer2": "set forwarding-options sampling family inet filter s1 term 1 then {\n    destination {\n        ip;\n    }\n    next-hop;\n    protocol;\n    port;\n    loss-priority low;\n    output-node 192.168.10.100;\n    flow-server 192.168.10.100 8888;\n    sampling-action random-detect 0.1;\n}\n\nThis Junos command sets up IPv4 traffic sampling on the router with instance s1 and sends the sampled data to the flow-server with the IP address 192.168.10.100 using port number 8888. The sampling action is set to random-detect with a drop probability of 0.1.\n\nNote: Make sure to replace the IP addresses and port numbers with the appropriate values for your network setup.\n\nPlease let me know if you need any further assistance.\u003c/s\u003e",
    "external_id": null,
    "metadata": null,
    "prompt": "\nYou, as a helpful AI assistant, will provide a detailed, step-by-step answer to the user\u0027s question.\n\nUSER: Create a Junos command that configures the router to enable IPv4 traffic sampling with instance s1, and directs the sampled data to be sent to the flow-server with the IP address 198.51.100.2 on port 2055.\n\nASSISTANT: \n",
    "relevant": {
        "status": [
            "submitted"
        ],
        "user_id": [
            "c8f4058e-7b43-43b2-ae9d-4dc8da3fc138"
        ],
        "value": [
            "answer1"
        ]
    }
}
```

### 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.
    
    * **prompt** is of type `TextField`.
    * **answer1** is of type `TextField`.
    * **answer2** 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.
    
    * **relevant** is of type `LabelQuestion`.

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

Please, read the question carefully and try to answer it as accurately as possible.

#### 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:n<1K rlfh argilla human-feedback region:us
{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}
TAGS
#size_categories-n<1K #rlfh #argilla #human-feedback #region-us 

Dataset Card for temp
=====================


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.


	+ prompt is of type 'TextField'.
	+ answer1 is of type 'TextField'.
	+ answer2 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.


	+ relevant is of type 'LabelQuestion'.


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


Please, read the question carefully and try to answer it as accurately as possible.


#### 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.


	+ prompt is of type 'TextField'.
	+ answer1 is of type 'TextField'.
	+ answer2 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.


	+ relevant is of type 'LabelQuestion'.


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


Please, read the question carefully and try to answer it as accurately as possible. #### 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-n<1K #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.


	+ prompt is of type 'TextField'.
	+ answer1 is of type 'TextField'.
	+ answer2 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.


	+ relevant is of type 'LabelQuestion'.


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


Please, read the question carefully and try to answer it as accurately as possible. #### 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