bert-base-multilingual-cased-ner-hrl Open-source Model - Supports Identification of Locations, Organizations, and People's Names in 10 Languages

Bert Base Multilingual Cased Ner Hrl

Developed by Davlan

A multilingual named entity recognition model based on mBERT, supporting 10 high-resource languages, capable of identifying three types of entities: locations, organizations, and person names.

Sequence Labeling

Transformers

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

Downloads 363.27k

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of bert-base-multilingual-cased 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: locations (LOC), organizations (ORG), and person names (PER).

Continuous Entity Handling

Can distinguish the starting positions of continuous entities of the same type.

Model Capabilities

Multilingual text processing

Named entity recognition

Entity classification

Use Cases

Information Extraction

News Article Entity Extraction

Extract key entity information from news articles.

Accurately identifies person names, organizations, and locations.

Multilingual Document Processing

Process named entities in multilingual documents.

Supports entity recognition in 10 languages.

🚀 bert-base-multilingual-cased-ner-hrl

bert-base-multilingual-cased-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 is based on a fine - tuned mBERT base model and can recognize three types of entities: location (LOC), organizations (ORG), and person (PER).

🚀 Quick Start

How to use

You can use this model with Transformers pipeline for NER.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-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: This model supports 10 high - resourced languages, making it suitable for a wide range of multilingual NER tasks.
Entity Recognition: It can recognize three types of entities: location (LOC), organizations (ORG), and person (PER).

📦 Installation

The installation steps are mainly about setting up the Transformers library. You can install it using the following command:

pip install transformers

📚 Documentation

Intended uses & limitations

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:

Property	Details
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:

Property	Details
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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