Metric Card for F1


## Metric Description

The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)


## How to Use

At minimum, this metric requires predictions and references as input

```python
>>> f1_metric = datasets.load_metric("f1")
>>> results = f1_metric.compute(predictions=[0, 1], references=[0, 1])
>>> print(results)
["{'f1': 1.0}"]
```
The context provides a clear and direct formula for calculating the F1 score: F1 = 2 * (precision * recall) / (precision + recall). This directly answers the question about the formula for the F1 score without any ambiguity.


The F1 score is a crucial metric for evaluating the performance of machine learning models, especially in NLP tasks where precision and recall are important. Understanding how to calculate the F1 score is essential for developers working with classification models in the Hugging Face ecosystem, as it helps in assessing model accuracy and making informed decisions about model improvements. Therefore, this question is highly relevant and useful for machine learning developers.


The question "What is the formula for calculating the F1 score?" is context-independent because it asks for a specific, well-defined piece of information that does not rely on any additional context to be understood. The F1 score is a standard metric used in statistics and machine learning, and its formula is universally recognized. Therefore, anyone with access to relevant documentation or knowledge in the field can understand and answer this question without needing further context.

