--- 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` and `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/r/f`, `sents_p/r/f`, `tag_acc`, `pos_acc`, `morph_acc`, `morph_per_feat`, `lemma_acc`, `dep_uas`, `dep_las`, `dep_las_per_type`, `ents_p/r/f`, `ents_per_type`, `textcat_macro_auc`, `textcat_macro_f`. > #### Example > > ```python > scorer = Scorer() > 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"} Scores the tokenization: - `token_acc`: # correct tokens / # gold tokens - `token_p/r/f`: PRF for token character spans | 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/p/r/f`. | ## Scorer.score_token_attr {#score_token_attr tag="staticmethod"} Scores a single token attribute. | 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 score `attr_acc`. | ## Scorer.score_token_attr_per_feat {#score_token_attr_per_feat tag="staticmethod"} Scores a single token attribute per feature for a token attribute in UFEATS format. | 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 unders the key `attr_per_feat`. | ## Scorer.score_spans {#score_spans tag="staticmethod"} Returns PRF scores for labeled or unlabeled spans. | 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/r/f` and the per-type PRF scores under `attr_per_type`. | ## Scorer.score_deps {#score_deps tag="staticmethod"} Calculate the UAS, LAS, and LAS per type scores for dependency parses. | 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"} 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. | 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`: 1) for 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) for binary exclusive with positive label: `attr_p/r/f`; 3) for 3+ exclusive classes, macro-averaged fscore: `attr_macro_f`; 4) for multilabel, macro-averaged AUC: `attr_macro_auc` |