size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:argilla region:us rlfh argilla human-feedback
---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for Gaurav12321







This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).


## Using this dataset 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.Dataset.from_hub("wyzard-ai/Gaurav12321", settings="auto")
```

This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.

## Using this dataset with `datasets`

To load the records of 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("wyzard-ai/Gaurav12321")
```

This will only load the records of the dataset, but not the Argilla settings.

## Dataset Structure

This dataset repo contains:

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` 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.
* A dataset configuration folder conforming to the Argilla dataset format in `.argilla`.

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

### Fields

The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| instruction | User instruction | text | True | True |


### Questions

The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| relevance_score | How Relevant is the conversation based upon expert. Is the conversation highly curated for you or not. Please don't judge accuracy. | rating | True | N/A | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| accuracy_score | How accurate is the conversation based upon persona | rating | True | if  | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| clarity_score | How clear is the conversation based upon persona | rating | True | Is the LLM getting confused | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| actionable_score | How actionable is the conversation based upon persona | rating | True | Is the LLM response to actionable for example, it shows comparison card on the right question.  | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| engagement_score | How engaging is the conversation based upon persona | rating | True | Are there a lot of question that are being shown if yes, high score else low score | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| completeness_score | is the conversation complete based upon persona | rating | True | is the conversation complete based upon persona, not leaving any key aspect out | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
| feedback | feedback | text | True | What do you think can be improved in the given conversation. How good was the conversation as per you? | N/A |


<!-- check length of metadata properties -->

### Metadata

The **metadata** is a dictionary that can be used to provide additional information about the dataset record.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
 | conv_id | Conversation ID |  |  -  | True |
 | turn | Conversation Turn |  | 0 - 100 | True |






### Data Instances

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

```json
{
    "_server_id": "f3afba2f-ecac-4403-8164-74570d5ec4aa",
    "fields": {
        "instruction": "**user**: hi\n**assistant**: Hello Gaurav! \ud83c\udf1f How\u0027s your day going? Are you exploring any new marketing software or digital trends today? Let\u0027s dive into the world of creative tools together!\n**user**: what are best marketing tools\n**assistant**: Hi Gaurav! \ud83d\ude80 Great to see your interest in marketing tools! Here\u2019s a list of some of the best marketing tools that can help enhance your strategies:\n\n| **Tool**                | **Purpose**                          | **Key Features**                                      |\n|-------------------------|--------------------------------------|------------------------------------------------------|\n| **HubSpot**             | Inbound Marketing                     | CRM, email marketing, content management, analytics   |\n| **Mailchimp**           | Email Marketing                       | Email automation, audience segmentation, analytics    |\n| **Hootsuite**           | Social Media Management              | Scheduling, monitoring, analytics for social media    |\n| **Google Analytics**    | Web Analytics                        | Traffic analysis, user behavior insights, reporting    |\n| **SEMrush**             | SEO \u0026 Content Marketing              | Keyword research, site audit, competitor analysis     |\n| **Canva**               | Graphic Design                       | Easy-to-use design templates for social media, ads    |\n| **Buffer**              | Social Media Scheduling              | Post scheduling, analytics, team collaboration        |\n| **Trello**              | Project Management                   | Organizing marketing campaigns, task management       |\n\n*Would you like to explore any specific tools from this list, or do you have particular marketing goals in mind that we can align with the right software?*\n**feedback**: sdfgjkldfk\n**user**: I specifically want data engineering tools\n**assistant**: To assist you with data engineering tools, here\u2019s a curated list of some of the best options available that can help streamline your data processes:\n\n| **Tool**                | **Purpose**                          | **Key Features**                                      |\n|-------------------------|--------------------------------------|------------------------------------------------------|\n| **Apache Spark**        | Big Data Processing                  | Fast processing, supports batch and streaming data   |\n| **Apache Kafka**        | Real-time Data Streaming             | High throughput, fault-tolerant messaging system     |\n| **Snowflake**           | Cloud Data Warehousing                | Scalable storage, data sharing, and analytics        |\n| **dbt (Data Build Tool)** | Data Transformation                | SQL-based transformations, version control, testing  |\n| **Apache Airflow**      | Workflow Automation                  | Scheduling, monitoring, and managing complex workflows|\n| **PostgreSQL**          | Relational Database Management       | Advanced SQL support, extensibility, and reliability  |\n| **MongoDB**             | NoSQL Database                       | Flexible schema, high availability, and scalability   |\n| **Amazon Redshift**     | Data Warehousing                     | Fast query performance, integration with AWS services |\n\n*Do any of these tools align with your current projects or needs? If you have specific requirements or challenges in data engineering, please share, and I can provide more tailored recommendations.*\n**user**: What do you think is ClickHouse a new and best option for faster queries ?\n**assistant**: ClickHouse is indeed a compelling option for faster queries, especially in the realm of data engineering and analytics. Here are some key points to consider:\n\n### **Why ClickHouse?**\n\n| **Feature**                | **Description**                                      |\n|----------------------------|------------------------------------------------------|\n| **Columnar Storage**       | Optimized for read-heavy workloads, allowing for faster data retrieval. |\n| **High Performance**       | Designed for real-time analytics, capable of processing billions of rows per second. |\n| **Scalability**            | Easily scales horizontally by adding more nodes to the cluster. |\n| **SQL Support**            | Uses a familiar SQL syntax, making it accessible for users with SQL experience. |\n| **Data Compression**       | Efficient storage through advanced compression techniques, reducing disk space usage. |\n| **Parallel Processing**    | Executes queries in parallel, significantly speeding up query execution times. |\n| **OLAP Capabilities**      | Specifically built for Online Analytical Processing (OLAP), making it ideal for analytical queries. |\n\n### **Use Cases**\n- **Real-time Analytics**: Ideal for applications requiring immediate insights from large datasets.\n- **Business Intelligence**: Supports complex queries and aggregations for BI tools.\n- **Log Analysis**: Efficiently processes and analyzes logs from various sources.\n\n### **Considerations**\n- **Learning Curve**: While SQL-based, there may be specific optimizations and features unique to ClickHouse that require some learning.\n- **Deployment**: Depending on your infrastructure, setting up and managing ClickHouse may require additional resources.\n\n*Would you like to explore how ClickHouse can specifically fit into your data engineering projects, or do you have any particular use cases in mind?*\n**user**: what do you think is the best option among ClickHouse and redshift ?\n**assistant**: It seems like you might have sent an incomplete message. If you have any specific questions or need further information about ClickHouse or any other data engineering tools, feel free to let me know! I\u0027m here to help.\n**user**: a\n**assistant**: Both ClickHouse and Amazon Redshift are powerful tools for data warehousing and analytics, but they cater to different needs and use cases. Below is a comparison table highlighting their key features and capabilities:\n\nnull{\"graph_type\": \"comparison table\", \"x_label\": \"Feature\", \"y_label\": \"Tool\", \"json_payload\": [{\"name\": \"ClickHouse\", \"info\": [{\"feature\": \"Columnar Storage\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"High Performance\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Scalability\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"SQL Support\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Data Compression\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Parallel Processing\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"OLAP Capabilities\", \"value\": true, \"subFeatures\": []}]}, {\"name\": \"Amazon Redshift\", \"info\": [{\"feature\": \"Columnar Storage\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"High Performance\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Scalability\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"SQL Support\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Data Compression\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Parallel Processing\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"OLAP Capabilities\", \"value\": true, \"subFeatures\": []}]}]}\n\n### Insights:\n\n1. **Performance**: ClickHouse is often noted for its superior performance in real-time analytics, processing billions of rows per second, while Redshift is optimized for large-scale data storage and analysis.\n\n2. **Scalability**: Both tools offer excellent scalability options, allowing users to expand their data processing capabilities as needed.\n\n3. **SQL Support**: Both ClickHouse and Redshift utilize SQL, making them accessible to users familiar with SQL syntax.\n\n4. **Use Cases**: ClickHouse is particularly strong in scenarios requiring real-time analytics and log analysis, while Redshift excels in traditional data warehousing tasks.\n\n5. **Deployment Complexity**: ClickHouse may require more engineering effort to set up and manage compared to the more managed service approach of Redshift.\n\nIf you have specific use cases or requirements in mind, I can help you further refine your choice or explore a demo of either tool."
    },
    "id": "1a62db61-a95d-4e1c-9481-288888b008a0",
    "metadata": {
        "conv_id": "2b62cec7-4780-4659-9beb-9fb80a111bcf",
        "turn": 0
    },
    "responses": {},
    "status": "pending",
    "suggestions": {},
    "vectors": {}
}
```

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

```json
{
    "_server_id": "f3afba2f-ecac-4403-8164-74570d5ec4aa",
    "conv_id": "2b62cec7-4780-4659-9beb-9fb80a111bcf",
    "id": "1a62db61-a95d-4e1c-9481-288888b008a0",
    "instruction": "**user**: hi\n**assistant**: Hello Gaurav! \ud83c\udf1f How\u0027s your day going? Are you exploring any new marketing software or digital trends today? Let\u0027s dive into the world of creative tools together!\n**user**: what are best marketing tools\n**assistant**: Hi Gaurav! \ud83d\ude80 Great to see your interest in marketing tools! Here\u2019s a list of some of the best marketing tools that can help enhance your strategies:\n\n| **Tool**                | **Purpose**                          | **Key Features**                                      |\n|-------------------------|--------------------------------------|------------------------------------------------------|\n| **HubSpot**             | Inbound Marketing                     | CRM, email marketing, content management, analytics   |\n| **Mailchimp**           | Email Marketing                       | Email automation, audience segmentation, analytics    |\n| **Hootsuite**           | Social Media Management              | Scheduling, monitoring, analytics for social media    |\n| **Google Analytics**    | Web Analytics                        | Traffic analysis, user behavior insights, reporting    |\n| **SEMrush**             | SEO \u0026 Content Marketing              | Keyword research, site audit, competitor analysis     |\n| **Canva**               | Graphic Design                       | Easy-to-use design templates for social media, ads    |\n| **Buffer**              | Social Media Scheduling              | Post scheduling, analytics, team collaboration        |\n| **Trello**              | Project Management                   | Organizing marketing campaigns, task management       |\n\n*Would you like to explore any specific tools from this list, or do you have particular marketing goals in mind that we can align with the right software?*\n**feedback**: sdfgjkldfk\n**user**: I specifically want data engineering tools\n**assistant**: To assist you with data engineering tools, here\u2019s a curated list of some of the best options available that can help streamline your data processes:\n\n| **Tool**                | **Purpose**                          | **Key Features**                                      |\n|-------------------------|--------------------------------------|------------------------------------------------------|\n| **Apache Spark**        | Big Data Processing                  | Fast processing, supports batch and streaming data   |\n| **Apache Kafka**        | Real-time Data Streaming             | High throughput, fault-tolerant messaging system     |\n| **Snowflake**           | Cloud Data Warehousing                | Scalable storage, data sharing, and analytics        |\n| **dbt (Data Build Tool)** | Data Transformation                | SQL-based transformations, version control, testing  |\n| **Apache Airflow**      | Workflow Automation                  | Scheduling, monitoring, and managing complex workflows|\n| **PostgreSQL**          | Relational Database Management       | Advanced SQL support, extensibility, and reliability  |\n| **MongoDB**             | NoSQL Database                       | Flexible schema, high availability, and scalability   |\n| **Amazon Redshift**     | Data Warehousing                     | Fast query performance, integration with AWS services |\n\n*Do any of these tools align with your current projects or needs? If you have specific requirements or challenges in data engineering, please share, and I can provide more tailored recommendations.*\n**user**: What do you think is ClickHouse a new and best option for faster queries ?\n**assistant**: ClickHouse is indeed a compelling option for faster queries, especially in the realm of data engineering and analytics. Here are some key points to consider:\n\n### **Why ClickHouse?**\n\n| **Feature**                | **Description**                                      |\n|----------------------------|------------------------------------------------------|\n| **Columnar Storage**       | Optimized for read-heavy workloads, allowing for faster data retrieval. |\n| **High Performance**       | Designed for real-time analytics, capable of processing billions of rows per second. |\n| **Scalability**            | Easily scales horizontally by adding more nodes to the cluster. |\n| **SQL Support**            | Uses a familiar SQL syntax, making it accessible for users with SQL experience. |\n| **Data Compression**       | Efficient storage through advanced compression techniques, reducing disk space usage. |\n| **Parallel Processing**    | Executes queries in parallel, significantly speeding up query execution times. |\n| **OLAP Capabilities**      | Specifically built for Online Analytical Processing (OLAP), making it ideal for analytical queries. |\n\n### **Use Cases**\n- **Real-time Analytics**: Ideal for applications requiring immediate insights from large datasets.\n- **Business Intelligence**: Supports complex queries and aggregations for BI tools.\n- **Log Analysis**: Efficiently processes and analyzes logs from various sources.\n\n### **Considerations**\n- **Learning Curve**: While SQL-based, there may be specific optimizations and features unique to ClickHouse that require some learning.\n- **Deployment**: Depending on your infrastructure, setting up and managing ClickHouse may require additional resources.\n\n*Would you like to explore how ClickHouse can specifically fit into your data engineering projects, or do you have any particular use cases in mind?*\n**user**: what do you think is the best option among ClickHouse and redshift ?\n**assistant**: It seems like you might have sent an incomplete message. If you have any specific questions or need further information about ClickHouse or any other data engineering tools, feel free to let me know! I\u0027m here to help.\n**user**: a\n**assistant**: Both ClickHouse and Amazon Redshift are powerful tools for data warehousing and analytics, but they cater to different needs and use cases. Below is a comparison table highlighting their key features and capabilities:\n\nnull{\"graph_type\": \"comparison table\", \"x_label\": \"Feature\", \"y_label\": \"Tool\", \"json_payload\": [{\"name\": \"ClickHouse\", \"info\": [{\"feature\": \"Columnar Storage\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"High Performance\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Scalability\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"SQL Support\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Data Compression\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Parallel Processing\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"OLAP Capabilities\", \"value\": true, \"subFeatures\": []}]}, {\"name\": \"Amazon Redshift\", \"info\": [{\"feature\": \"Columnar Storage\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"High Performance\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Scalability\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"SQL Support\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Data Compression\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"Parallel Processing\", \"value\": true, \"subFeatures\": []}, {\"feature\": \"OLAP Capabilities\", \"value\": true, \"subFeatures\": []}]}]}\n\n### Insights:\n\n1. **Performance**: ClickHouse is often noted for its superior performance in real-time analytics, processing billions of rows per second, while Redshift is optimized for large-scale data storage and analysis.\n\n2. **Scalability**: Both tools offer excellent scalability options, allowing users to expand their data processing capabilities as needed.\n\n3. **SQL Support**: Both ClickHouse and Redshift utilize SQL, making them accessible to users familiar with SQL syntax.\n\n4. **Use Cases**: ClickHouse is particularly strong in scenarios requiring real-time analytics and log analysis, while Redshift excels in traditional data warehousing tasks.\n\n5. **Deployment Complexity**: ClickHouse may require more engineering effort to set up and manage compared to the more managed service approach of Redshift.\n\nIf you have specific use cases or requirements in mind, I can help you further refine your choice or explore a demo of either tool.",
    "status": "pending",
    "turn": 0
}
```


### 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

Review the user interactions with the chatbot.

#### 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]