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  • ar
  • de
  • en
  • es
  • fr
  • it
  • lv
  • nl
  • pt
  • zh
  • multilingual


Model description

distilbert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a distilbert-base-multilingual-cased model that was fine-tuned on an aggregation of 10 high-resourced languages

Intended uses & limitations

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)

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.

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