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97 lines
4.1 KiB
Markdown
97 lines
4.1 KiB
Markdown
---
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title: Corpus
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teaser: An annotated corpus
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tag: class
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source: spacy/gold/corpus.py
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new: 3
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---
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This class manages annotated corpora and can read training and development
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datasets in the [DocBin](/api/docbin) (`.spacy`) format.
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## Corpus.\_\_init\_\_ {#init tag="method"}
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Create a `Corpus`. The input data can be a file or a directory of files.
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> #### Example
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>
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> ```python
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> from spacy.gold import Corpus
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>
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> corpus = Corpus("./train.spacy", "./dev.spacy")
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> ```
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| Name | Type | Description |
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| ------- | ------------ | ---------------------------------------------------------------- |
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| `train` | str / `Path` | Training data (`.spacy` file or directory of `.spacy` files). |
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| `dev` | str / `Path` | Development data (`.spacy` file or directory of `.spacy` files). |
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| `limit` | int | Maximum number of examples returned. `0` for no limit (default). |
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## Corpus.train_dataset {#train_dataset tag="method"}
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Yield examples from the training data.
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> #### Example
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>
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> ```python
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> from spacy.gold import Corpus
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> import spacy
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>
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> corpus = Corpus("./train.spacy", "./dev.spacy")
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> nlp = spacy.blank("en")
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> train_data = corpus.train_dataset(nlp)
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> ```
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| Name | Type | Description |
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| -------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `nlp` | `Language` | The current `nlp` object. |
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| _keyword-only_ | | |
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| `shuffle` | bool | Whether to shuffle the examples. Defaults to `True`. |
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| `gold_preproc` | bool | Whether to train on gold-standard sentences and tokens. Defaults to `False`. |
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| `max_length` | int | Maximum document length. Longer documents will be split into sentences, if sentence boundaries are available. `0` for no limit (default). |
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| **YIELDS** | `Example` | The examples. |
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## Corpus.dev_dataset {#dev_dataset tag="method"}
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Yield examples from the development data.
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> #### Example
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>
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> ```python
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> from spacy.gold import Corpus
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> import spacy
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>
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> corpus = Corpus("./train.spacy", "./dev.spacy")
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> nlp = spacy.blank("en")
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> dev_data = corpus.dev_dataset(nlp)
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> ```
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| Name | Type | Description |
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| -------------- | ---------- | ---------------------------------------------------------------------------- |
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| `nlp` | `Language` | The current `nlp` object. |
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| _keyword-only_ | | |
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| `gold_preproc` | bool | Whether to train on gold-standard sentences and tokens. Defaults to `False`. |
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| **YIELDS** | `Example` | The examples. |
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## Corpus.count_train {#count_train tag="method"}
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Get the word count of all training examples.
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> #### Example
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>
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> ```python
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> from spacy.gold import Corpus
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> import spacy
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>
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> corpus = Corpus("./train.spacy", "./dev.spacy")
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> nlp = spacy.blank("en")
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> word_count = corpus.count_train(nlp)
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> ```
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| Name | Type | Description |
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| ----------- | ---------- | ------------------------- |
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| `nlp` | `Language` | The current `nlp` object. |
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| **RETURNS** | int | The word count. |
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<!-- TODO: document remaining methods? / decide which to document -->
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