Multilingual XLM-RoBERTa for NER: An Open-Source Named Entity Recognition Model - Multilingual Support for Identifying Locations, Organizations, and Persons

Multilingual Xlm Roberta For Ner

Developed by Tirendaz

A named entity recognition model fine-tuned based on the XLM-RoBERTa base model, supporting multiple languages and capable of identifying three types of entities: locations, organizations, and persons.

Sequence Labeling

Transformers

Open Source License:MIT #Multilingual NER #High-precision entity recognition #XLM-RoBERTa fine-tuning

Downloads 56

Release Time : 10/21/2023

Model Overview

This model is a multilingual named entity recognition model based on the XLM-RoBERTa architecture, fine-tuned on aggregated data from 10 high-resource languages. It is primarily used to identify three types of entities in text: locations (LOC), organizations (ORG), and persons (PER).

Model Features

Multilingual support

Trained on aggregated data from 10 high-resource languages, capable of multilingual named entity recognition

High-precision recognition

Achieves an F1 score of 0.8607 on the PAN-X.de validation set, demonstrating excellent performance

Lightweight deployment

Based on the XLM-RoBERTa base model, relatively lightweight and easy to deploy

Model Capabilities

Multilingual text processing

Named entity recognition

Entity classification (LOC/ORG/PER)

Use Cases

Information extraction

News text analysis

Extract key persons, organizations, and locations from news articles

Accurately identify named entities and their categories in text

Document automation processing

Automate the processing of entity information in multilingual documents

Improve document processing efficiency and accuracy

Knowledge graph construction

Knowledge graph entity extraction

Extract entities from unstructured text for knowledge graph construction

Provide structured entity data for knowledge graphs

🚀 multilingual-xlm-roberta-for-ner

This is a Named Entity Recognition model that leverages a fine - tuned XLM - RoBERTa base model. It's trained to identify three entity types: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a XLMRoreberta - base - multilingual - cased model fine - tuned on an aggregation of 10 high - resourced languages.

🚀 Quick Start

You can use this model with Transformers pipeline for NER.

Basic Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Tirendaz/roberta-base-NER")
model = AutoModelForTokenClassification.from_pretrained("Tirendaz/roberta-base-NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)

✨ Features

This model can recognize three types of named entities: location (LOC), organizations (ORG), and person (PER).

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Tirendaz/roberta-base-NER")
model = AutoModelForTokenClassification.from_pretrained("Tirendaz/roberta-base-NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)

Advanced Usage

No advanced usage code is provided in the original document, so this part is skipped.

📚 Documentation

Abbreviations

Abbreviation	Description
O	Outside of a named entity
B - PER	Beginning of a person’s name right after another person’s name
I - PER	Person’s name
B - ORG	Beginning of an organisation right after another organisation
I - ORG	Organisation
B - LOC	Beginning of a location right after another location
I - LOC	Location

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 48
eval_batch_size: 48
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	263	0.1627	0.8229
0.214	2.0	526	0.1410	0.8472
0.214	3.0	789	0.1343	0.8608

Framework versions

Transformers 4.33.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

🔧 Technical Details

No specific technical details beyond what's already covered are provided, so this section is skipped.

📄 License

This model is released under the MIT license.

📊 Model Information

Property	Details
Model Type	Multilingual XLM - RoBERTa for NER
Training Data	xtreme
Metrics	F1
Model Name	multilingual - xlm - roberta - for - ner
Results	Task: Token Classification on xtreme (PAN - X.de validation split), F1: 0.8607623700505596

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