---
title: Scorer
teaser: Compute evaluation scores
tag: class
source: spacy/scorer.py
---

The `Scorer` computes evaluation scores. It's typically created by
[`Language.evaluate`](/api/language#evaluate). In addition, the `Scorer`
provides a number of evaluation methods for evaluating [`Token`](/api/token) and
[`Doc`](/api/doc) attributes.

## Scorer.\_\_init\_\_ {#init tag="method"}

Create a new `Scorer`.

> #### Example
>
> ```python
> from spacy.scorer import Scorer
>
> # Default scoring pipeline
> scorer = Scorer()
>
> # Provided scoring pipeline
> nlp = spacy.load("en_core_web_sm")
> scorer = Scorer(nlp)
> ```

| Name  | Type     | Description                                                                                                                                                                                                                                                            |
| ----- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nlp` | Language | The pipeline to use for scoring, where each pipeline component may provide a scoring method. If none is provided, then a default pipeline for the multi-language code `xx` is constructed containing: `senter`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat`. |

## Scorer.score {#score tag="method"}

Calculate the scores for a list of [`Example`](/api/example) objects using the
scoring methods provided by the components in the pipeline.

The returned `Dict` contains the scores provided by the individual pipeline
components. For the scoring methods provided by the `Scorer` and use by the core
pipeline components, the individual score names start with the `Token` or `Doc`
attribute being scored:

- `token_acc`, `token_p`, `token_r`, `token_f`,
- `sents_p`, `sents_r`, `sents_f`
- `tag_acc`, `pos_acc`, `morph_acc`, `morph_per_feat`, `lemma_acc`
- `dep_uas`, `dep_las`, `dep_las_per_type`
- `ents_p`, `ents_r` `ents_f`, `ents_per_type`
- `textcat_macro_auc`, `textcat_macro_f`

> #### Example
>
> ```python
> scorer = Scorer()
> scores = scorer.score(examples)
> ```

| Name        | Type                | Description                                                                                   |
| ----------- | ------------------- | --------------------------------------------------------------------------------------------- |
| `examples`  | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| **RETURNS** | `Dict`              | A dictionary of scores.                                                                       |

## Scorer.score_tokenization {#score_tokenization tag="staticmethod" new="3"}

Scores the tokenization:

- `token_acc`: number of correct tokens / number of gold tokens
- `token_p`, `token_r`, `token_f`: precision, recall and F-score for token
  character spans

> #### Example
>
> ```python
> scores = Scorer.score_tokenization(examples)
> ```

| Name        | Type                | Description                                                                                   |
| ----------- | ------------------- | --------------------------------------------------------------------------------------------- |
| `examples`  | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| **RETURNS** | `Dict`              | A dictionary containing the scores `token_acc`, `token_p`, `token_r`, `token_f`.              |

## Scorer.score_token_attr {#score_token_attr tag="staticmethod" new="3"}

Scores a single token attribute.

> #### Example
>
> ```python
> scores = Scorer.score_token_attr(examples, "pos")
> print(scores["pos_acc"])
> ```

| Name           | Type                | Description                                                                                                                   |
| -------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `examples`     | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
| `attr`         | `str`               | The attribute to score.                                                                                                       |
| `getter`       | `Callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
| **RETURNS**    | `Dict[str, float]`  | A dictionary containing the score `{attr}_acc`.                                                                               |

## Scorer.score_token_attr_per_feat {#score_token_attr_per_feat tag="staticmethod" new="3"}

Scores a single token attribute per feature for a token attribute in
[UFEATS](https://universaldependencies.org/format.html#morphological-annotation)
format.

> #### Example
>
> ```python
> scores = Scorer.score_token_attr_per_feat(examples, "morph")
> print(scores["morph_per_feat"])
> ```

| Name           | Type                | Description                                                                                                                   |
| -------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `examples`     | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
| `attr`         | `str`               | The attribute to score.                                                                                                       |
| `getter`       | `Callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
| **RETURNS**    | `Dict`              | A dictionary containing the per-feature PRF scores under the key `{attr}_per_feat`.                                           |

## Scorer.score_spans {#score_spans tag="staticmethod" new="3"}

Returns PRF scores for labeled or unlabeled spans.

> #### Example
>
> ```python
> scores = Scorer.score_spans(examples, "ents")
> print(scores["ents_f"])
> ```

| Name           | Type                | Description                                                                                                                                   |
| -------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples`     | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                                 |
| `attr`         | `str`               | The attribute to score.                                                                                                                       |
| `getter`       | `Callable`          | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`.                             |
| **RETURNS**    | `Dict`              | A dictionary containing the PRF scores under the keys `{attr}_p`, `{attr}_r`, `{attr}_f` and the per-type PRF scores under `{attr}_per_type`. |

## Scorer.score_deps {#score_deps tag="staticmethod" new="3"}

Calculate the UAS, LAS, and LAS per type scores for dependency parses.

> #### Example
>
> ```python
> def dep_getter(token, attr):
>     dep = getattr(token, attr)
>     dep = token.vocab.strings.as_string(dep).lower()
>     return dep
>
> scores = Scorer.score_deps(
>     examples,
>     "dep",
>     getter=dep_getter,
>     ignore_labels=("p", "punct")
> )
> print(scores["dep_uas"], scores["dep_las"])
> ```

| Name            | Type                | Description                                                                                                                   |
| --------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `examples`      | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
| `attr`          | `str`               | The attribute containing the dependency label.                                                                                |
| `getter`        | `Callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
| `head_attr`     | `str`               | The attribute containing the head token.                                                                                      |
| `head_getter`   | `callable`          | Defaults to `getattr`. If provided, `head_getter(token, attr)` should return the head for an individual `Token`.              |
| `ignore_labels` | `Tuple`             | Labels to ignore while scoring (e.g., `punct`).                                                                               |
| **RETURNS**     | `Dict`              | A dictionary containing the scores: `{attr}_uas`, `{attr}_las`, and `{attr}_las_per_type`.                                    |

## Scorer.score_cats {#score_cats tag="staticmethod" new="3"}

Calculate PRF and ROC AUC scores for a doc-level attribute that is a dict
containing scores for each label like `Doc.cats`. The reported overall score
depends on the scorer settings:

1. **all:** `{attr}_score` (one of `{attr}_f` / `{attr}_macro_f` /
   `{attr}_macro_auc`), `{attr}_score_desc` (text description of the overall
   score), `{attr}_f_per_type`, `{attr}_auc_per_type`
2. **binary exclusive with positive label:** `{attr}_p`, `{attr}_r`, `{attr}_f`
3. **3+ exclusive classes**, macro-averaged F-score: `{attr}_macro_f`;
4. **multilabel**, macro-averaged AUC: `{attr}_macro_auc`

> #### Example
>
> ```python
> labels = ["LABEL_A", "LABEL_B", "LABEL_C"]
> scores = Scorer.score_cats(
>     examples,
>     "cats",
>     labels=labels
> )
> print(scores["cats_macro_auc"])
> ```

| Name             | Type                | Description                                                                                             |
| ---------------- | ------------------- | ------------------------------------------------------------------------------------------------------- |
| `examples`       | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.           |
| `attr`           | `str`               | The attribute to score.                                                                                 |
| `getter`         | `Callable`          | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. |
| labels           | `Iterable[str]`     | The set of possible labels. Defaults to `[]`.                                                           |
| `multi_label`    | `bool`              | Whether the attribute allows multiple labels. Defaults to `True`.                                       |
| `positive_label` | `str`               | The positive label for a binary task with exclusive classes. Defaults to `None`.                        |
| **RETURNS**      | `Dict`              | A dictionary containing the scores, with inapplicable scores as `None`.                                 |