README.md

---
license: cc0-1.0
task_categories:
- token-classification
language:
- en
tags:
- named-entity-recognition
- ner
- scientific
- unit-conversion
- units
- measurement
- natural-language-understanding
- automatic-annotations
---
# Natural Unit Conversion Dataset

## Dataset Overview

This dataset contains unit conversion requests, where each example includes a sentence with associated 
entities (in spaCy-supported format) for Named-Entity Recognition (NER) modeling. The entities represent the 
values and units being converted. The goal is to aid in developing systems capable of extracting unit conversion 
data from natural language for natural language understanding.

The data is structured with the following fields:

- `text`: The sentence containing the unit conversion request natural text.
- `entities`: A list of entity annotations. Each entity includes:
  - The start and end (exclusive) positions of the entity in the text.
  - The entity type (e.g., `UNIT_VALUE`, `FROM_UNIT`, `TO_UNIT`, `FEET_VALUE`, `INCH_VALUE`).

## Examples

```json
[
  {
    "text": "I'd like to know 8284 atm converted to pascals", 
    "entities": [
      [39, 46, "TO_UNIT"], 
      [17, 21, "UNIT_VALUE"], 
      [22, 25, "FROM_UNIT"]
    ]
  }, 
  {
    "text": "Convert 3'6\" to fts", 
    "entities": [
      [8, 9, "FEET_VALUE"], 
      [10, 11, "INCH_VALUE"], 
      [16, 19, "TO_UNIT"]
    ]
  },
  {
    "text": "Convert this: 4487 n-m to poundal meters",
    "entities": [
      [26, 40, "TO_UNIT"], 
      [14, 18, "UNIT_VALUE"], 
      [19, 22, "FROM_UNIT"]
    ]
  },
]
```

### Loading Dataset
You can load the dataset `maliknaik/natural_unit_conversion` using the following code:

```python
from datasets import load_dataset

dataset_name = "maliknaik/natural_unit_conversion"
dataset = load_dataset(dataset_name)
```


### Entity Types
- **UNIT_VALUE**: Represents the value to be converted.
- **FROM_UNIT**: The unit from which the conversion is being made.
- **TO_UNIT**: The unit to which the conversion is being made.
- **FEET_VALUE**: The value representing feet in a length conversion.
- **INCH_VALUE**: The value representing inches in a length conversion.


### Dataset Split and Sampling

The dataset is split using Stratified Sampling to ensure that the distribution of entities is consistent across 
training, validation, and test splits. This method helps to ensure that each split contains representative examples
of all entity types.

- Training Set: **583,863** samples
- Validation Set: **100,091** samples
- Test Set: **150,137** samples


### Supported Units
The dataset supports a variety of units for conversion, including but not limited to:

**Length**: inches, feet, yards, meters, centimeters, millimeters, micrometers, kilometers, miles, mils

**Area**: square meters, square inches, square feet, square yard, square centimeters, square miles, square kilometers, square feet us, square millimeters, hectares, acres, are

**Mass**: ounces, kilograms, pounds, tons, grams, ettograms, centigrams, milligrams, carats, quintals, pennyweights, troy ounces, uma, stones,, micrograms

**Volume**: cubic meters, liters, us gallons, imperial gallons, us pints, imperial pints, us quarts, deciliters, centiliters, milliliters, microliters, tablespoons us, australian tablespoons, cups, cubic millimeters, cubic centimeters, cubic inches, cubic feet, us fluid ounces, imperial fluid ounces, us gill, imperial gill

**Temperature**: celsius, fahrenheit, kelvin, reamur, romer, delisle, rankine

**Time**: seconds, minutes, hours, days, weeks, lustrum, decades, centuries, millennium, deciseconds, centiseconds, milliseconds, microseconds, nanoseconds

**Speed**: kilometers per hour, miles per hour, meters per second, feets per second, knots, minutes per kilometer

**Pressure**: atmosphere, bar, millibar, psi, pascal, kilo pascal, torr, inch of mercury, hecto pascal

**Force**: newton, kilogram force, pound force, dyne, poundal

**Energy**: kilowatt hours, kilocalories, calories, joules, kilojoules, electronvolts, energy foot pound

**Power**: kilowatt, european horse power, imperial horse power, watt, megawatt, gigawatt, milliwatt

**Torque**: newton meter, kilogram force meter, dyne meter, pound force feet, poundal meter

**Angle**: degree, radians

**Digital**: byte, bit, nibble, kilobyte, gigabyte, terabyte, petabyte, exabyte, tebibit, exbibit, etc

**Fuel_efficiency**: kilometers per liter, liters per100km, miles per us gallon, miles per imperial gallon

**Shoe_size**: eu china, usa canada child, usa canada man, usa canada woman, uk india child, uk india man, uk india woman, japan

### Usage
This dataset can be used for training named entity recognition (NER) models, especially for tasks related to unit 
conversion and natural language understanding.

### License
This dataset is available under the CC0-1.0 license. It is free to use for any purpose without any restrictions.

### Citation
If you use this dataset in your work, please cite it as follows:

```
@misc{unit-conversion-dataset,
  author = {Malik N. Mohammed},
  title = {Natural Language Unit Conversion Dataset for Named-Entity Recognition},
  year = {2025},
  publisher = {HuggingFace},
  journal = {HuggingFace repository}
  howpublished = {\url{https://huggingface.co/datasets/maliknaik/natural_unit_conversion}}
}

```