# 🤗 Dataset Card: fhirfly/medicalquestions

## Dataset Overview

- Dataset name: fhirfly/medicalquestions
- Dataset size: 25,102 questions
- Labels: 1 (medical), 0 (non-medical)
- Distribution: Evenly distributed between medical and non-medical questions

## Dataset Description

The fhirfly/medicalquestions dataset is a collection of 25,102 questions labeled as either medical or non-medical. The dataset aims to provide a diverse range of questions covering various medical and non-medical domains.

The questions in the dataset have been manually labeled by domain experts based on the context and content of each question. Each question is assigned a label of 1 if it is determined to be a medical question and a label of 0 if it is classified as a non-medical question.

## Dataset Structure

The dataset consists of a single file containing the following columns:

- **Text**: The text of the question.
- **Label**: The label assigned to each question, either 1 (medical) or 0 (non-medical).

The questions are evenly distributed between medical and non-medical categories, ensuring a balanced dataset for training and evaluation.

## Potential Biases

Efforts have been made to ensure that the dataset is representative of various medical and non-medical topics. However, it is important to acknowledge that biases may exist in the dataset due to the subjective nature of labeling questions. Biases could be present in terms of the types of questions included, the representation of certain medical conditions or non-medical topics, or the labeling process itself.

It is recommended to perform thorough evaluation and analysis of the dataset to identify and mitigate potential biases during model training and deployment. Care should be taken to address any biases to ensure fair and unbiased predictions.

## Dataset Quality

The fhirfly/medicalquestions dataset has undergone manual labeling by domain experts, which helps maintain a high level of quality and accuracy. However, human labeling is not entirely immune to errors or subjectivity.

To ensure the quality of the dataset, a thorough review process has been conducted to minimize errors and maintain consistency in labeling. Nonetheless, it is advisable to validate and verify the data as part of your specific use case to ensure it meets your requirements.

## Data License

The fhirfly/medicalquestions dataset is released under the MIT license. Please refer to the license file accompanying the dataset for more information on its usage and any restrictions that may apply.

## Dataset Citation

If you use the fhirfly/medicalquestions dataset in your work, please cite it as:
```
@dataset{fhirfly/medicalquestions,
  title = {fhirfly/medicalquestions},
  author = {fhirfly},
  year = {2023},
  publisher = {Hugging Face},
  version = {1.0.0},
  url = {https://huggingface.co/datasets/fhirfly/medicalquestions}
}
```
task_categories:text-classification size_categories:10K<n<100K language:en license:mit medical region:us
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "FhirFly Medical Questions", "tags": ["medical"]}
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #medical #region-us 

#  Dataset Card: fhirfly/medicalquestions

## Dataset Overview

- Dataset name: fhirfly/medicalquestions
- Dataset size: 25,102 questions
- Labels: 1 (medical), 0 (non-medical)
- Distribution: Evenly distributed between medical and non-medical questions

## Dataset Description

The fhirfly/medicalquestions dataset is a collection of 25,102 questions labeled as either medical or non-medical. The dataset aims to provide a diverse range of questions covering various medical and non-medical domains.

The questions in the dataset have been manually labeled by domain experts based on the context and content of each question. Each question is assigned a label of 1 if it is determined to be a medical question and a label of 0 if it is classified as a non-medical question.

## Dataset Structure

The dataset consists of a single file containing the following columns:

- Text: The text of the question.
- Label: The label assigned to each question, either 1 (medical) or 0 (non-medical).

The questions are evenly distributed between medical and non-medical categories, ensuring a balanced dataset for training and evaluation.

## Potential Biases

Efforts have been made to ensure that the dataset is representative of various medical and non-medical topics. However, it is important to acknowledge that biases may exist in the dataset due to the subjective nature of labeling questions. Biases could be present in terms of the types of questions included, the representation of certain medical conditions or non-medical topics, or the labeling process itself.

It is recommended to perform thorough evaluation and analysis of the dataset to identify and mitigate potential biases during model training and deployment. Care should be taken to address any biases to ensure fair and unbiased predictions.

## Dataset Quality

The fhirfly/medicalquestions dataset has undergone manual labeling by domain experts, which helps maintain a high level of quality and accuracy. However, human labeling is not entirely immune to errors or subjectivity.

To ensure the quality of the dataset, a thorough review process has been conducted to minimize errors and maintain consistency in labeling. Nonetheless, it is advisable to validate and verify the data as part of your specific use case to ensure it meets your requirements.

## Data License

The fhirfly/medicalquestions dataset is released under the MIT license. Please refer to the license file accompanying the dataset for more information on its usage and any restrictions that may apply.

## Dataset Citation

If you use the fhirfly/medicalquestions dataset in your work, please cite it as:

#  Dataset Card: fhirfly/medicalquestions ## Dataset Overview

- Dataset name: fhirfly/medicalquestions
- Dataset size: 25,102 questions
- Labels: 1 (medical), 0 (non-medical)
- Distribution: Evenly distributed between medical and non-medical questions ## Dataset Description

The fhirfly/medicalquestions dataset is a collection of 25,102 questions labeled as either medical or non-medical. The dataset aims to provide a diverse range of questions covering various medical and non-medical domains.

The questions in the dataset have been manually labeled by domain experts based on the context and content of each question. Each question is assigned a label of 1 if it is determined to be a medical question and a label of 0 if it is classified as a non-medical question. ## Dataset Structure

The dataset consists of a single file containing the following columns:

- Text: The text of the question.
- Label: The label assigned to each question, either 1 (medical) or 0 (non-medical).

The questions are evenly distributed between medical and non-medical categories, ensuring a balanced dataset for training and evaluation. ## Potential Biases

Efforts have been made to ensure that the dataset is representative of various medical and non-medical topics. However, it is important to acknowledge that biases may exist in the dataset due to the subjective nature of labeling questions. Biases could be present in terms of the types of questions included, the representation of certain medical conditions or non-medical topics, or the labeling process itself.

It is recommended to perform thorough evaluation and analysis of the dataset to identify and mitigate potential biases during model training and deployment. Care should be taken to address any biases to ensure fair and unbiased predictions. ## Dataset Quality

The fhirfly/medicalquestions dataset has undergone manual labeling by domain experts, which helps maintain a high level of quality and accuracy. However, human labeling is not entirely immune to errors or subjectivity.

To ensure the quality of the dataset, a thorough review process has been conducted to minimize errors and maintain consistency in labeling. Nonetheless, it is advisable to validate and verify the data as part of your specific use case to ensure it meets your requirements. ## Data License

The fhirfly/medicalquestions dataset is released under the MIT license. Please refer to the license file accompanying the dataset for more information on its usage and any restrictions that may apply. ## Dataset Citation

If you use the fhirfly/medicalquestions dataset in your work, please cite it as:
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #medical #region-us 
 #  Dataset Card: fhirfly/medicalquestions ## Dataset Overview

- Dataset name: fhirfly/medicalquestions
- Dataset size: 25,102 questions
- Labels: 1 (medical), 0 (non-medical)
- Distribution: Evenly distributed between medical and non-medical questions ## Dataset Description

The fhirfly/medicalquestions dataset is a collection of 25,102 questions labeled as either medical or non-medical. The dataset aims to provide a diverse range of questions covering various medical and non-medical domains.

The questions in the dataset have been manually labeled by domain experts based on the context and content of each question. Each question is assigned a label of 1 if it is determined to be a medical question and a label of 0 if it is classified as a non-medical question. ## Dataset Structure

The dataset consists of a single file containing the following columns:

- Text: The text of the question.
- Label: The label assigned to each question, either 1 (medical) or 0 (non-medical).

The questions are evenly distributed between medical and non-medical categories, ensuring a balanced dataset for training and evaluation. ## Potential Biases

Efforts have been made to ensure that the dataset is representative of various medical and non-medical topics. However, it is important to acknowledge that biases may exist in the dataset due to the subjective nature of labeling questions. Biases could be present in terms of the types of questions included, the representation of certain medical conditions or non-medical topics, or the labeling process itself.

It is recommended to perform thorough evaluation and analysis of the dataset to identify and mitigate potential biases during model training and deployment. Care should be taken to address any biases to ensure fair and unbiased predictions. ## Dataset Quality

The fhirfly/medicalquestions dataset has undergone manual labeling by domain experts, which helps maintain a high level of quality and accuracy. However, human labeling is not entirely immune to errors or subjectivity.

To ensure the quality of the dataset, a thorough review process has been conducted to minimize errors and maintain consistency in labeling. Nonetheless, it is advisable to validate and verify the data as part of your specific use case to ensure it meets your requirements. ## Data License

The fhirfly/medicalquestions dataset is released under the MIT license. Please refer to the license file accompanying the dataset for more information on its usage and any restrictions that may apply. ## Dataset Citation

If you use the fhirfly/medicalquestions dataset in your work, please cite it as:
passage: TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #medical #region-us 
#  Dataset Card: fhirfly/medicalquestions## Dataset Overview

- Dataset name: fhirfly/medicalquestions
- Dataset size: 25,102 questions
- Labels: 1 (medical), 0 (non-medical)
- Distribution: Evenly distributed between medical and non-medical questions## Dataset Description

The fhirfly/medicalquestions dataset is a collection of 25,102 questions labeled as either medical or non-medical. The dataset aims to provide a diverse range of questions covering various medical and non-medical domains.

The questions in the dataset have been manually labeled by domain experts based on the context and content of each question. Each question is assigned a label of 1 if it is determined to be a medical question and a label of 0 if it is classified as a non-medical question.## Dataset Structure

The dataset consists of a single file containing the following columns:

- Text: The text of the question.
- Label: The label assigned to each question, either 1 (medical) or 0 (non-medical).

The questions are evenly distributed between medical and non-medical categories, ensuring a balanced dataset for training and evaluation.## Potential Biases

Efforts have been made to ensure that the dataset is representative of various medical and non-medical topics. However, it is important to acknowledge that biases may exist in the dataset due to the subjective nature of labeling questions. Biases could be present in terms of the types of questions included, the representation of certain medical conditions or non-medical topics, or the labeling process itself.

It is recommended to perform thorough evaluation and analysis of the dataset to identify and mitigate potential biases during model training and deployment. Care should be taken to address any biases to ensure fair and unbiased predictions.