AI is happening. The future is updating.distilbert-base-multilingual-cased-ner-hrl
Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **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 You can use this model with Transformers *pipeline* for NER. 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. The training data for the 10 languages are from:
Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **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 You can use this model with Transformers *pipeline* for NER. 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. The training data for the 10 languages are from: