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NERAggregateMetrics

anno_eval::eval::evaluator

Struct NERAggregateMetrics 

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pub struct NERAggregateMetrics {
Show 18 fields
    pub precision: MetricValue,
    pub recall: MetricValue,
    pub f1: MetricValue,
    pub macro_precision: MetricValue,
    pub macro_recall: MetricValue,
    pub macro_f1: MetricValue,
    pub precision_std: f64,
    pub recall_std: f64,
    pub f1_std: f64,
    pub precision_ci_95: Option<(f64, f64)>,
    pub recall_ci_95: Option<(f64, f64)>,
    pub f1_ci_95: Option<(f64, f64)>,
    pub per_type: HashMap<String, TypeMetrics>,
    pub tokens_per_second: f64,
    pub num_test_cases: usize,
    pub total_found: usize,
    pub total_expected: usize,
    pub total_correct: usize,

}
Expand description

Aggregated NER evaluation metrics with statistical measures.

Provides mean, standard deviation, and confidence intervals for comprehensive analysis.

§Micro vs Macro Averaging

By default, we compute micro-averaged metrics (total_correct / total_found), which is the standard for NER evaluation and matches seqeval’s default.

Macro-averaging (average of per-case metrics) can inflate scores when test cases have different sizes. A test case with 1 entity getting 100% F1 shouldn’t boost overall metrics as much as a case with 100 entities getting 50% F1.

Fields§

§precision: MetricValue

Micro-averaged precision (total_correct / total_found)

§recall: MetricValue

Micro-averaged recall (total_correct / total_expected)

§f1: MetricValue

Micro-averaged F1 score

§macro_precision: MetricValue

Macro-averaged precision (for comparison)

§macro_recall: MetricValue

Macro-averaged recall (for comparison)

§macro_f1: MetricValue

Macro-averaged F1 (for comparison)

§precision_std: f64

Precision standard deviation (of per-case metrics)

§recall_std: f64

Recall standard deviation (of per-case metrics)

§f1_std: f64

F1 standard deviation (of per-case metrics)

§precision_ci_95: Option<(f64, f64)>

Precision 95% confidence interval (lower, upper)

§recall_ci_95: Option<(f64, f64)>

Recall 95% confidence interval (lower, upper)

§f1_ci_95: Option<(f64, f64)>

F1 95% confidence interval (lower, upper)

§per_type: HashMap<String, TypeMetrics>

Per-entity-type aggregated metrics (micro-averaged)

§tokens_per_second: f64

Mean tokens per second

§num_test_cases: usize

Number of test cases evaluated

§total_found: usize

Total entities found

§total_expected: usize

Total entities expected

§total_correct: usize

Total correct predictions

Trait Implementations§

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impl Clone for NERAggregateMetrics

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fn clone(&self) -> NERAggregateMetrics

Returns a duplicate of the value. Read more
1.0.0 (const: unstable) · Source§

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for NERAggregateMetrics

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl<'de> Deserialize<'de> for NERAggregateMetrics

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fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>
where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer. Read more
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impl Serialize for NERAggregateMetrics

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fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>
where __S: Serializer,

Serialize this value into the given Serde serializer. Read more

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