AIが動く。未来が更新される。heBERT_NER
HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config. HeBert was trained on three dataset: 1. A Hebrew version of OSCAR: ~9.8 GB of data, including 1 billion words and over 20.8 millions sentences. 2. A Hebrew dump of Wikipedia: ~650 MB of data, including over 63 millions words and 3.8 millions sentences 3. Emotion User Generated Content (UGC) data that was collected for the purpose of this study (described below). The ability of the model to classify named entities in text, such as persons' names, organizations, and locations; tested on a labeled dataset from Ben Mordecai and M Elhadad (2005), and evaluated with F1-score.
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HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config. HeBert was trained on three dataset: 1. A Hebrew version of OSCAR: ~9.8 GB of data, including 1 billion words and over 20.8 millions sentences. 2. A Hebrew dump of Wikipedia: ~650 MB of data, including over 63 millions words and 3.8 millions sentences 3. Emotion User Generated Content (UGC) data that was collected for the purpose of this study (described below). The ability of the model to classify named entities in text, such as persons' names, organizations, and locations; tested on a labeled dataset from Ben Mordecai and M Elhadad (2005), and evaluated with F1-score.