# spaCy
**Supported Labeling Types**: `Span labeling`
Spacy provides NLP pipeline optimized for named entity recognition (NER), dependency parsing, and tokenization. It can be used for a variety of information extraction and NLP tasks, making it a powerful tool for automated text processing and analysis.
ML Assisted with SpaCy
### Model Details
* Suitable for span labeling projects, which involve extracting meaningful text spans such as names, dates, and monetary values.
* Uses the [`en_core_web_lg`](https://spacy.io/models/en#en_core_web_lg) model where the "lg" (large) variant includes 685,000 unique vectors with 300 dimensions, providing exceptional semantic understanding capabilities.
* Built on diverse web content including news articles, blogs, and commentary, ensuring broad coverage across different text types.
* Hosted locally within the Datasaur Intelligence container, eliminating external dependencies and network latency.
### **Usage**
* This model is a pre-trained large English NLP model, containing word vectors and trained for entity recognition, part-of-speech tagging, and syntactic analysis.
* The `en_core_web_lg` model includes the following named entity categories:
* `PERSON` – Individuals, including full names.
* `NORP` – Nationalities, religious, or political groups.
* `FACILITY` – Buildings, airports, highways, bridges, etc.
* `ORG` – Organizations such as companies, agencies, institutions.
* `GPE` – Geopolitical entities like countries, cities, and states.
* `LOC` – Non-GPE locations, mountain ranges, bodies of water.
* `PRODUCT` – Objects, vehicles, foods, etc. (Not services.)
* `EVENT` – Named events such as hurricanes, wars, sports events.
* `WORK_OF_ART` – Titles of books, songs, paintings, etc.
* `LAW` – Named laws, treaties, or legal documents.
* `LANGUAGE` – Any named language.
* `DATE` – Absolute or relative dates or periods.
* `TIME` – Specific times of day.
* `PERCENT` – Percentage values.
* `MONEY` – Monetary values.
* `QUANTITY` – Measurements of weight, distance, volume, etc.
* `ORDINAL` – First, second, third, etc.
* `CARDINAL` – Numerals that do not fall under other categories.
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.datasaur.ai/assisted-labeling/ml-assisted-labeling/spacy.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
