---
license: creativeml-openrail-m
task_categories:
- table-question-answering
- token-classification
- text-classification
- translation
- feature-extraction
- text2text-generation
- text-generation
- text-to-speech
- text-to-audio
- fill-mask
- summarization
- zero-shot-classification
- question-answering
- object-detection
- sentence-similarity
tags:
- code
pretty_name: pyvert1
size_categories:
- 10B<n<100B
---

# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

This dataset contains a Python script, "csv_to_training_file.py," which facilitates the conversion of structured data from CSV files into the required format for training models in the Hugging Face framework.

- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** CreativeML OpenRAIL-M

### Dataset Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

This dataset can be used to automate the process of converting structured data from CSV files into the required format for training models in the Hugging Face framework.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

This dataset should not be used for purposes other than its intended functionality, such as data manipulation or processing unrelated to model training file creation.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset structure consists of a Python script that contains functions for processing CSV files and generating the necessary training files for Hugging Face models.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The dataset was created to provide a tool for efficiently converting structured data from CSV files into the required format for training models in the Hugging Face framework.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

The source data for the Python script includes CSV files containing structured information that needs to be transformed into Hugging Face's model training files.

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

The source data producers include public records, professional networking platforms, and other publicly available sources.

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

N/A

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

The dataset does not contain personal or sensitive information beyond the inherent structure and functionality of the Python script.

## Bias, Risks, and Limitations

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The dataset may have limitations in terms of the accuracy and currency of the contact information. There is a risk of misuse if the data is used for unsolicited communication.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should exercise caution and respect privacy when using the contact information from this dataset. It is recommended to verify the contact information before use.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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APA:

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Glossary [optional]

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More Information [optional]

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Dataset Card Authors [optional]

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Dataset Card Contact

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