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distilbert-base-multilingual-cased-ner-hrl
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)
print(ner_results)
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:
Arabic: ANERcorp German: conll 2003 English: conll 2003 Spanish: conll 2002 French: Europeana Newspapers Italian: 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.