en_core_web_md Open-source English Processing Model - Free Deployment for Part-of-Speech and Entity Recognition Analysis

En Core Web Md

Developed by spacy

spaCy's medium-sized English processing pipeline, optimized for CPU, including POS tagging, dependency parsing, named entity recognition, etc.

Sequence Labeling EnglishOpen Source License:MIT #English Text Processing #Multi-task NLP #Dependency Parsing

Downloads 433

Release Time : 3/2/2022

Model Overview

This is a medium-sized English natural language processing model with a complete text processing pipeline, suitable for various NLP tasks. The model is optimized for CPU usage and ideal for production environment deployment.

Model Features

CPU Optimization

Specifically optimized for CPU usage scenarios, suitable for production environment deployment

Complete Processing Pipeline

Includes a complete NLP processing pipeline from tokenization to named entity recognition

High-Quality Word Vectors

Contains 20000 unique word vectors (300-dimensional), trained on multi-source data

Multi-Task Support

Supports various NLP tasks including POS tagging, dependency parsing, named entity recognition, etc.

Model Capabilities

POS Tagging

Dependency Parsing

Named Entity Recognition

Sentence Segmentation

Lemmatization

Text Processing

Use Cases

Text Analysis

News Content Analysis

Extract named entities (people, organizations, locations, etc.) from news text

NER F-score reaches 85.22%

Syntax Analysis

Analyze sentence structure and part-of-speech relationships

Dependency parsing UAS reaches 92.05%

Information Extraction

Document Processing

Extract key information and entities from documents

Supports recognition of 18 entity types

🚀 en_core_web_md

English pipeline optimized for CPU, featuring multiple NLP components for various tasks.

📚 Documentation

Details: https://spacy.io/models/en#en_core_web_md

English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.

Property	Details
Name	`en_core_web_md`
Version	`3.7.1`
spaCy	`>=3.7.2,<3.8.0`
Default Pipeline	`tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`
Components	`tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner`
Vectors	514157 keys, 20000 unique vectors (300 dimensions)
Sources	OntoNotes 5 (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston) ClearNLP Constituent-to-Dependency Conversion (Emory University) WordNet 3.0 (Princeton University) Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl) (Explosion)
License	`MIT`
Author	Explosion

Label Scheme

View label scheme (113 labels for 3 components)

Component	Labels
`tagger`	`$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ````
`parser`	`ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp`
`ner`	`CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART`

Accuracy

Type	Score
`TOKEN_ACC`	99.86
`TOKEN_P`	99.57
`TOKEN_R`	99.58
`TOKEN_F`	99.57
`TAG_ACC`	97.33
`SENTS_P`	92.21
`SENTS_R`	89.37
`SENTS_F`	90.77
`DEP_UAS`	92.05
`DEP_LAS`	90.23
`ENTS_P`	84.94
`ENTS_R`	85.49
`ENTS_F`	85.22

📄 License

This project is licensed under the MIT license.

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