distilbert-base-multilingual-cased-ner-hrl Open Source Named Entity Recognition Model - Identify Locations, Organizations, and People in 10 Languages

Distilbert Base Multilingual Cased Ner Hrl

Developed by Davlan

A named entity recognition model for 10 high-resource languages, based on a fine-tuned Distil BERT base model, capable of identifying three types of entities: locations, organizations, and persons.

Sequence Labeling

Transformers

#Multilingual NER #High-resource language support #News domain entity recognition

Downloads 270.56k

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned DistilBERT model on aggregated data from 10 high-resource languages, specifically designed for named entity recognition tasks, supporting the identification of LOC, ORG, and PER entity types.

Model Features

Multilingual Support

Supports named entity recognition in 10 high-resource languages, including Arabic, Chinese, and more.

Lightweight Model

Based on the DistilBERT architecture, it is more lightweight compared to the original BERT model while maintaining high performance.

Entity Type Recognition

Accurately identifies three types of entities: locations (LOC), organizations (ORG), and persons (PER).

Model Capabilities

Multilingual text processing

Named entity recognition

Sequence labeling

Use Cases

Information Extraction

News Article Entity Extraction

Extracts key entity information such as persons, organizations, and locations from multilingual news articles.

Accurately identifies named entities and their types in the text

Text Analysis

Multilingual Document Processing

Processes documents containing multiple languages to extract key entity information.

Supports entity recognition in 10 languages

🚀 distilbert-base-multilingual-cased-ner-hrl

distilbert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model. It can recognize entities in 10 high - resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese, and Chinese). This model is based on a fine - tuned Distilled BERT base model and can identify 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/distilbert-base-multilingual-cased-ner-hrl")
model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-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, including Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese, and Chinese.
Entity Recognition: It can recognize three types of entities: location (LOC), organizations (ORG), and person (PER).

📚 Documentation

Model description

This model is a distilbert - base - multilingual - cased model fine - tuned on an aggregation of 10 high - resourced languages.

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:

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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