| Name | date-spacy JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | https://github.com/wjbmattingly/date-spacy |
| Summary | A spaCy extension for enhanced date and number entity recognition and extraction as structured data. |
| upload_time | 2023-08-24 17:00:38 |
| maintainer | |
| docs_url | None |
| author | WJB Mattingly |
| requires_python | |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
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| coveralls test coverage |
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|
# Date spaCy
Date spaCy is a collection of custom spaCy pipeline component that enables you to easily identify date entities in a text and fetch the parsed date values using spaCy's token extensions. It uses RegEx to find dates and then uses the [dateparser](https://dateparser.readthedocs.io/en/latest/) library to convert those dates into structured datetime data. One current limitation is that if no year is given, it presumes it is the current year. The `dateparser` output is stored in a custom entity extension: `._.date`.
This lightweight approach can be added to an existing spaCy pipeline or to a blank model. If using in an existing spaCy pipeline, be sure to add it before the NER model.
## Installation
To install `date_spacy`, simply run:
```bash
pip install date-spacy
```
## Usage
### Adding the Component to your spaCy Pipeline
First, you'll need to import the `find_dates` component and add it to your spaCy pipeline:
```python
import spacy
from date_spacy import find_dates
# Load your desired spaCy model
nlp = spacy.blank('en')
# Add the component to the pipeline
nlp.add_pipe('find_dates')
```
### Processing Text with the Pipeline
After adding the component, you can process text as usual:
```python
doc = nlp("""The event is scheduled for 25th August 2023.
We also have a meeting on 10 September and another one on the twelfth of October and a
final one on January fourth.""")
```
### Accessing the Parsed Dates
You can iterate over the entities in the `doc` and access the special date extension:
```python
for ent in doc.ents:
if ent.label_ == "DATE":
print(f"Text: {ent.text} -> Parsed Date: {ent._.date}")
```
This will output:
```
Text: 25th August 2023 -> Parsed Date: 2023-08-25 00:00:00
Text: 10 September -> Parsed Date: 2023-09-10 00:00:00
Text: twelfth of October -> Parsed Date: 2023-10-12 00:00:00
Text: January fourth -> Parsed Date: 2023-01-04 00:00:00
```
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"description": "# Date spaCy\r\n\r\n\r\n\r\nDate spaCy is a collection of custom spaCy pipeline component that enables you to easily identify date entities in a text and fetch the parsed date values using spaCy's token extensions. It uses RegEx to find dates and then uses the [dateparser](https://dateparser.readthedocs.io/en/latest/) library to convert those dates into structured datetime data. One current limitation is that if no year is given, it presumes it is the current year. The `dateparser` output is stored in a custom entity extension: `._.date`.\r\n\r\nThis lightweight approach can be added to an existing spaCy pipeline or to a blank model. If using in an existing spaCy pipeline, be sure to add it before the NER model.\r\n\r\n## Installation\r\n\r\nTo install `date_spacy`, simply run:\r\n\r\n```bash\r\npip install date-spacy\r\n```\r\n\r\n## Usage\r\n\r\n### Adding the Component to your spaCy Pipeline\r\n\r\nFirst, you'll need to import the `find_dates` component and add it to your spaCy pipeline:\r\n\r\n```python\r\nimport spacy\r\nfrom date_spacy import find_dates\r\n\r\n# Load your desired spaCy model\r\nnlp = spacy.blank('en')\r\n\r\n# Add the component to the pipeline\r\nnlp.add_pipe('find_dates')\r\n```\r\n\r\n### Processing Text with the Pipeline\r\n\r\nAfter adding the component, you can process text as usual:\r\n\r\n```python\r\ndoc = nlp(\"\"\"The event is scheduled for 25th August 2023.\r\n We also have a meeting on 10 September and another one on the twelfth of October and a\r\n final one on January fourth.\"\"\")\r\n```\r\n\r\n### Accessing the Parsed Dates\r\n\r\nYou can iterate over the entities in the `doc` and access the special date extension:\r\n\r\n```python\r\nfor ent in doc.ents:\r\n if ent.label_ == \"DATE\":\r\n print(f\"Text: {ent.text} -> Parsed Date: {ent._.date}\")\r\n```\r\n\r\nThis will output:\r\n\r\n```\r\nText: 25th August 2023 -> Parsed Date: 2023-08-25 00:00:00\r\nText: 10 September -> Parsed Date: 2023-09-10 00:00:00\r\nText: twelfth of October -> Parsed Date: 2023-10-12 00:00:00\r\nText: January fourth -> Parsed Date: 2023-01-04 00:00:00\r\n```\r\n",
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