
# Dataset Card for llama-2-banking-fine-tune

This dataset has been created with [Argilla](https://docs.argilla.io).

As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).

## Dataset Description

- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This dataset contains:

* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.

* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.

### Load with Argilla

To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:

```python
import argilla as rg

ds = rg.FeedbackDataset.from_huggingface("argilla/llama-2-banking-fine-tune")
```

### Load with `datasets`

To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:

```python
from datasets import load_dataset

ds = load_dataset("argilla/llama-2-banking-fine-tune")
```

### Supported Tasks and Leaderboards

This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).

There are no leaderboards associated with this dataset.

### Languages

[More Information Needed]

## Dataset Structure

### Data in Argilla

The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.

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

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| request | User request | TextField | True | False |
| response-1 | Assistant response 1 | TextField | True | True |
| response-2 | Assistant response 2 | TextField | True | True |


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

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| preference | Order response based on your preference | RankingQuestion | True | 1 = most accurate and helpful, 2 = worst. Ties are allowed. | ['response-1', 'response-2'] |
| correct-response | If both responses are innacurate, complete the response: | TextQuestion | True | N/A | N/A |


**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.

Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.

### Data Instances

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

```json
{
    "external_id": null,
    "fields": {
        "request": "I tried to make a transfer but it failed",
        "response-1": " Thank you for choosing the Bank of America for your financial needs! I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? This will help me better understand the issue and assist you in resolving it as quickly as possible. Additionally, have you tried checking your account balance or recent transactions to see if the transfer has been processed? If you have any further questions or concerns, please don\u0027t hesitate to ask. Thank you for trusting the Bank of America with your financial matters!",
        "response-2": " Hello there! Thank you for choosing the Bank of America for your financial needs. I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? Additionally, do you have any error messages or confirmations that you received after attempting the transfer? This information will help me better understand the issue and assist you in resolving it as quickly as possible. Thank you for trusting the Bank of America with your financial matters."
    },
    "id": null,
    "metadata": {},
    "responses": [],
    "suggestions": [
        {
            "agent": null,
            "question_id": "b80fb550-1add-4ad6-93c9-b403e6342306",
            "question_name": "preference",
            "score": null,
            "type": null,
            "value": [
                {
                    "rank": 1,
                    "value": "response-2"
                },
                {
                    "rank": 2,
                    "value": "response-1"
                }
            ]
        }
    ]
}
```

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

```json
{
    "correct-response": null,
    "correct-response-suggestion": null,
    "correct-response-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "external_id": null,
    "metadata": null,
    "preference": null,
    "preference-suggestion": {
        "rank": [
            1,
            2
        ],
        "value": [
            "response-2",
            "response-1"
        ]
    },
    "preference-suggestion-metadata": {
        "agent": null,
        "score": null,
        "type": null
    },
    "request": "I tried to make a transfer but it failed",
    "response-1": " Thank you for choosing the Bank of America for your financial needs! I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? This will help me better understand the issue and assist you in resolving it as quickly as possible. Additionally, have you tried checking your account balance or recent transactions to see if the transfer has been processed? If you have any further questions or concerns, please don\u0027t hesitate to ask. Thank you for trusting the Bank of America with your financial matters!",
    "response-2": " Hello there! Thank you for choosing the Bank of America for your financial needs. I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? Additionally, do you have any error messages or confirmations that you received after attempting the transfer? This information will help me better understand the issue and assist you in resolving it as quickly as possible. Thank you for trusting the Bank of America with your financial matters."
}
```

### 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.
    
    * **request** is of type `TextField`.
    * **response-1** is of type `TextField`.
    * **response-2** is of type `TextField`.

* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
    
    * **preference** is of type `RankingQuestion` with the following allowed values ['response-1', 'response-2'], and description "1 = most accurate and helpful, 2 = worst. Ties are allowed.".
    * (optional) **correct-response** is of type `TextQuestion`.

* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
    
    * (optional) **preference-suggestion** is of type `ranking` with the following allowed values ['response-1', 'response-2'].
    * (optional) **correct-response-suggestion** is of type `text`.

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

* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.

### Data Splits

The dataset contains a single split, which is `train`.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation guidelines

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 llama-2-banking-fine-tune
==========================================


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, suggestions, and guidelines.


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



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.



NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.


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


	+ request is of type 'TextField'.
	+ response-1 is of type 'TextField'.
	+ response-2 is of type 'TextField'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.


	+ preference is of type 'RankingQuestion' with the following allowed values ['response-1', 'response-2'], and description "1 = most accurate and helpful, 2 = worst. Ties are allowed.".
	+ (optional) correct-response is of type 'TextQuestion'.
* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.


	+ (optional) preference-suggestion is of type 'ranking' with the following allowed values ['response-1', 'response-2'].
	+ (optional) correct-response-suggestion is of type 'text'.


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


* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.


### Data Splits


The dataset contains a single split, which is 'train'.


Dataset Creation
----------------


### Curation Rationale


### 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, suggestions, and guidelines.


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



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.



NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.


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


	+ request is of type 'TextField'.
	+ response-1 is of type 'TextField'.
	+ response-2 is of type 'TextField'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.


	+ preference is of type 'RankingQuestion' with the following allowed values ['response-1', 'response-2'], and description "1 = most accurate and helpful, 2 = worst. Ties are allowed.".
	+ (optional) correct-response is of type 'TextQuestion'.
* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.


	+ (optional) preference-suggestion is of type 'ranking' with the following allowed values ['response-1', 'response-2'].
	+ (optional) correct-response-suggestion is of type 'text'.


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


* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits


The dataset contains a single split, which is 'train'.


Dataset Creation
---------------- ### Curation Rationale ### 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, suggestions, and guidelines.


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



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.



NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.


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


	+ request is of type 'TextField'.
	+ response-1 is of type 'TextField'.
	+ response-2 is of type 'TextField'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.


	+ preference is of type 'RankingQuestion' with the following allowed values ['response-1', 'response-2'], and description "1 = most accurate and helpful, 2 = worst. Ties are allowed.".
	+ (optional) correct-response is of type 'TextQuestion'.
* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.


	+ (optional) preference-suggestion is of type 'ranking' with the following allowed values ['response-1', 'response-2'].
	+ (optional) correct-response-suggestion is of type 'text'.


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


* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits


The dataset contains a single split, which is 'train'.


Dataset Creation
---------------- ### Curation Rationale ### 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
passage: 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
----------------- passage: ### Data in Argilla


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


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



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.



NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.


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


	+ request is of type 'TextField'.
	+ response-1 is of type 'TextField'.
	+ response-2 is of type 'TextField'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.


	+ preference is of type 'RankingQuestion' with the following allowed values ['response-1', 'response-2'], and description "1 = most accurate and helpful, 2 = worst. Ties are allowed.".
	+ (optional) correct-response is of type 'TextQuestion'.
* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.


	+ (optional) preference-suggestion is of type 'ranking' with the following allowed values ['response-1', 'response-2'].
	+ (optional) correct-response-suggestion is of type 'text'.


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


* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.### Data Splits


The dataset contains a single split, which is 'train'.


Dataset Creation
----------------### Curation Rationale### Source Data