xlm-roberta-base-ner-hrl Open Source Model - Supports Location, Organization, and Person Entity Recognition in 10 Languages

Xlm Roberta Base Ner Hrl

Developed by Davlan

A multilingual named entity recognition model based on XLM-RoBERTa, supporting 10 high-resource languages, capable of identifying three types of entities: location, organization, and person

Sequence Labeling

Transformers

#Multilingual NER #High-resource languages #News domain entity recognition

Downloads 21.81k

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned XLM-RoBERTa base model on aggregated data from 10 high-resource languages, specifically designed for named entity recognition tasks

Model Features

Multilingual Support

Supports named entity recognition in 10 high-resource languages

Entity Type Recognition

Accurately identifies three types of entities: location (LOC), organization (ORG), and person (PER)

Continuous Entity Handling

Can distinguish between the start and continuation parts of the same type of entity appearing consecutively

Model Capabilities

Multilingual text analysis

Named entity recognition

Entity classification

Use Cases

Text Analysis

News Article Entity Extraction

Extracts key entity information from multilingual news articles

Accurately identifies person names, organizations, and locations

Multilingual Document Processing

Processes documents containing multiple languages and extracts entities

Supports mixed-text processing in 10 languages

🚀 xlm-roberta-base-ner-hrl

xlm-roberta-base-ner-hrl is a Named Entity Recognition model designed for 10 high-resourced languages, including Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese, and Chinese. It's based on a fine-tuned XLM-RoBERTa base model and can recognize three types of entities: location (LOC), organizations (ORG), and person (PER).

🚀 Quick Start

You can use this model with Transformers pipeline for NER.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-ner-hrl")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
ner_results = nlp(example)
print(ner_results)

✨ Features

Multilingual Support: Capable of performing Named Entity Recognition for 10 high-resourced languages.
Entity Recognition: Trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).

📚 Documentation

Model description

xlm-roberta-base-ner-hrl is a xlm-roberta-base model fine-tuned on an aggregation of 10 high-resourced languages.

Intended uses & limitations

How to use

As shown in the quick start section, you can use the model with Transformers pipeline for NER.

Limitations and bias

This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.

Training data

The training data for the 10 languages are from:

Language	Dataset
Arabic	ANERcorp
German	conll 2003
English	conll 2003
Spanish	conll 2002
French	Europeana Newspapers
Italian	Italian I-CAB
Latvian	Latvian NER
Dutch	conll 2002
Portuguese	Paramopama + Second Harem
Chinese	MSRA

The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:

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 procedure

This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

📄 License

This model is licensed under the afl-3.0 license.

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