en_core_web_trf Open-Source English NLP Model - Free to Use for Functions like Named Entity Recognition

En Core Web Trf

Developed by spacy

A Transformer-based English NLP pipeline model providing high-performance named entity recognition, part-of-speech tagging, and dependency parsing.

Sequence Labeling EnglishOpen Source License:MIT #Transformer Architecture #High-precision NER #English Text Processing

Downloads 185

Release Time : 3/2/2022

Model Overview

This model is an English Transformer pipeline in the spaCy library, built on the RoBERTa-base architecture, supporting various natural language processing tasks including named entity recognition, part-of-speech tagging, dependency parsing, and sentence segmentation.

Model Features

High-performance Transformer Architecture

Based on the RoBERTa-base Transformer architecture, providing more accurate semantic understanding and context capturing capabilities.

Multi-task Processing

A single model supports multiple NLP tasks including named entity recognition, part-of-speech tagging, and dependency parsing.

High Accuracy

Achieves 90.19% F1-score in named entity recognition tasks and 98.13% accuracy in part-of-speech tagging.

Model Capabilities

Named Entity Recognition

Part-of-Speech Tagging

Dependency Parsing

Sentence Segmentation

Text Token Classification

Use Cases

Text Analysis

Information Extraction

Extract named entities such as person names, locations, and organizations from text

Accurately identifies over 90% of named entities

Syntax Analysis

Analyze the grammatical structure and part-of-speech of sentences

Part-of-speech tagging accuracy exceeds 98%

Content Processing

Document Preprocessing

Prepare text data for machine learning tasks

Provides high-quality tokenized text

🚀 en_core_web_trf

The en_core_web_trf is an English transformer pipeline in spaCy, designed for various token - classification tasks such as NER, tagging, and dependency parsing. It offers high - accuracy performance in multiple linguistic analysis tasks.

🚀 Quick Start

For more details about this model, please visit: https://spacy.io/models/en#en_core_web_trf

✨ Features

Transformer - based: Utilizes the roberta - base transformer for powerful feature extraction.
Multiple Components: Includes components like transformer, tagger, parser, ner, attribute_ruler, and lemmatizer.
High Accuracy: Achieves high scores in various token - classification tasks.

📦 Installation

The installation details are not provided in the original document, so this section is skipped.

💻 Usage Examples

The usage examples are not provided in the original document, so this section is skipped.

📚 Documentation

Model Information

Property	Details
Model Name	`en_core_web_trf`
Version	`3.7.3`
spaCy Compatibility	`>=3.7.2,<3.8.0`
Default Pipeline	`transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`
Components	`transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`
Vectors	0 keys, 0 unique vectors (0 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](https://github.com/clir/clearnlp - guidelines/blob/master/md/components/dependency_conversion.md) (Emory University) WordNet 3.0 (Princeton University) roberta - base (Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov)
License	`MIT`
Author	Explosion

Label Scheme

View label scheme (112 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`, ````
`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`	98.13
`SENTS_P`	94.89
`SENTS_R`	85.79
`SENTS_F`	90.11
`DEP_UAS`	95.26
`DEP_LAS`	93.91
`ENTS_P`	90.08
`ENTS_R`	90.30
`ENTS_F`	90.19

🔧 Technical Details

The English transformer pipeline is based on the roberta - base model. It uses a byte - BPE piece encoder with a stride of 104, a window of 144, and a vocabulary size of 50265. The pipeline consists of multiple components that work together to perform tasks such as part - of - speech tagging, dependency parsing, and named entity recognition.

📄 License

This project is licensed under the MIT license.

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