Davlan/bert-base-multilingual-cased-ner-hrl NER命名实体识别模型

Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- # bert-base-multilingual-cased-ner-hrl ## Model description **bert-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 mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *bert-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/bert-base-multilingual-cased-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-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.