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
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)
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.
The training data for the 10 languages are from:
|Portuguese||Paramopama + Second Harem|
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:
|O||Outside of a named entity|
|B-PER||Beginning of a person’s name right after another person’s name|
|B-ORG||Beginning of an organisation right after another organisation|
|B-LOC||Beginning of a location right after another location|
This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.