| Hugging Face's logo | |
| --- | |
| language: | |
| - ar | |
| - de | |
| - en | |
| - es | |
| - fr | |
| - it | |
| - lv | |
| - nl | |
| - pt | |
| - zh | |
| - multilingual | |
| --- | |
| # 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. | |
| ```python | |
| 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: | |
| Language|Dataset | |
| -|- | |
| Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) | |
| German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) | |
| English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) | |
| Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) | |
| French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio) | |
| Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html) | |
| Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities) | |
| Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) | |
| Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese) | |
| Chinese | [MSRA](https://huggingface.co/datasets/msra_ner) | |
| 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. | |