64 lines
3.0 KiB
Markdown
64 lines
3.0 KiB
Markdown
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Hugging Face's logo
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---
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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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Arabic: [ANERcorp](https://github.com/EmnamoR/Arabic-named-entity-recognition)
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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: []()
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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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-|-
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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.
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