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license: afl-3.0 |
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Hugging Face's logo |
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language: |
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- ar |
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- de |
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- en |
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- es |
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- fr |
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- it |
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- lv |
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- nl |
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- pt |
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- zh |
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- multilingual |
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--- |
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# distilbert-base-multilingual-cased-ner-hrl |
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## Model description |
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**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). |
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Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages |
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## Intended uses & limitations |
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#### How to use |
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You can use this model with Transformers *pipeline* for NER. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl") |
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model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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#### Limitations and bias |
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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. |
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## Training data |
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The training data for the 10 languages are from: |
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Language|Dataset |
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Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) |
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German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) |
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English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) |
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Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) |
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French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio) |
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Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html) |
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Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities) |
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Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) |
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Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese) |
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Chinese | [MSRA](https://huggingface.co/datasets/msra_ner) |
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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: |
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Abbreviation|Description |
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O|Outside of a named entity |
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B-PER |Beginning of a person’s name right after another person’s name |
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I-PER |Person’s name |
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B-ORG |Beginning of an organisation right after another organisation |
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I-ORG |Organisation |
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B-LOC |Beginning of a location right after another location |
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I-LOC |Location |
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## Training procedure |
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This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code. |
